1 - datasets_preparation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4fc65f93",
   "metadata": {},
   "source": [
    "# Librairies and functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cb6a8f28",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import igraph as ig\n",
    "import networkx as nx\n",
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt\n",
    "import json, re, gc, requests, os\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cea33a4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.ion()\n",
    "plt.rc('text', usetex=True)\n",
    "plt.rc('font', family='serif')\n",
    "n = 2\n",
    "plt.rc('axes', linewidth=n)\n",
    "ticksSizeMajor = 10\n",
    "ticksSizeMinor = 5\n",
    "ticksWidth = n\n",
    "ticksLabelSize = 20\n",
    "axisLabelSize = 25\n",
    "titleSize = 30\n",
    "legendSize = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6fd647bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "path_input = r'/my/path/input'\n",
    "path_output = r'/my/path/output'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75f35975",
   "metadata": {},
   "source": [
    "# 1 - Open the main files\n",
    "\n",
    "- `Papers_neuroscience_all.json` \n",
    "- `Papers_neuroscience_and_AI.json`\n",
    "- `ExtraPapers_from_authors.csv`\n",
    "- `citationNetwork.csv`\n",
    "- `journals_MAG2WOS.csv`\n",
    "- `AI_kw_classification.txt`\n",
    "- `author2paper.csv`\n",
    "- `authorId_name_affiliation.csv`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3607a2b3",
   "metadata": {},
   "source": [
    "## 1.1 - Read the whole large database of neuroscience papers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9568d414",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(f'{path_input}Papers_neuroscience_all.json', 'r') as f:\n",
    "    data = json.load(f)\n",
    "data = pd.DataFrame.from_dict(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bbd46ac4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', ' publicationDate', ' issueIdentifier', ' created', ' publisher',\n",
       "       ' volume', ' startingPage', ' endingPage', ' citationCount', ' doi',\n",
       "       ' title', ' referenceCount', ' appearsInJournal', 'queryLevel',\n",
       "       'authors', ' bookTitle', ' appearsInConferenceSeries',\n",
       "       ' appearsInConferenceInstance'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11d53124",
   "metadata": {},
   "source": [
    "## 1.2 - Read the whole citation network containing no isolated nodes, ie. total degree $\\neq$ 0\n",
    "Here one citation link is directed such that 'citing' is 'source' and 'cited' is 'target'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ad43626",
   "metadata": {},
   "outputs": [],
   "source": [
    "CN = ig.Graph.DataFrame(pd.read_csv(f'{path_input}citationNetwork.csv', sep=',', usecols=['citing', 'cited']), directed=True)\n",
    "nodes = [CN.vs[i]['name'] for i in range(len(CN.vs))]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d844c6a",
   "metadata": {},
   "source": [
    "# 2 - Build a global egocentric dataset for citation network study\n",
    "Build a subdataframe containing only papers containing at least one edge (coming inside or outside) in the whole citation network. We consider here only first 'in' and 'out' level of citation.\n",
    "\n",
    "The index \"queryLevel\" index indicates here the presence of a given article inside the originally extracted neuroscience dataset (value 0, egocentric) or not (value 1, those added for the citation network)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d769a163",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>queryLevel</th>\n",
       "      <th>title</th>\n",
       "      <th>publicationYear</th>\n",
       "      <th>idJournal</th>\n",
       "      <th>authors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2038431747</td>\n",
       "      <td>0</td>\n",
       "      <td>\"Topology learning solved by extended objects...</td>\n",
       "      <td>1994</td>\n",
       "      <td>207023548</td>\n",
       "      <td>[2163422316, 2419355596, 2289589767]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1010827267</td>\n",
       "      <td>0</td>\n",
       "      <td>\"Multipotent Stem Cells In Murine Sclera\"</td>\n",
       "      <td>2011</td>\n",
       "      <td>76304953</td>\n",
       "      <td>[2482935619, 2618323644, 2661080946, 266108094...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2411724805</td>\n",
       "      <td>0</td>\n",
       "      <td>\"Comparative study on results of prefrontal l...</td>\n",
       "      <td>1953</td>\n",
       "      <td>124451323</td>\n",
       "      <td>[2987759242, 2631162769, 2651065461]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2004877318</td>\n",
       "      <td>0</td>\n",
       "      <td>\"Ventral frontal deficits in psychopathy: Neu...</td>\n",
       "      <td>1995</td>\n",
       "      <td>165368631</td>\n",
       "      <td>[2572627602, 2278535667, 2800225512]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2025848838</td>\n",
       "      <td>0</td>\n",
       "      <td>\"Iowa gambling task in Parkinson&amp;#039</td>\n",
       "      <td>2011</td>\n",
       "      <td>35508115</td>\n",
       "      <td>[237217217, 2129310074, 2647734675]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>14028712</th>\n",
       "      <td>2080374763</td>\n",
       "      <td>1</td>\n",
       "      <td>\"The spectral sensitivity of light-adapted ca...</td>\n",
       "      <td>1954</td>\n",
       "      <td>2090548</td>\n",
       "      <td>[2684787159]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14028713</th>\n",
       "      <td>2147483632</td>\n",
       "      <td>1</td>\n",
       "      <td>\"The 5-hydroxytryptamine (serotonin) receptor...</td>\n",
       "      <td>2013</td>\n",
       "      <td>136368880</td>\n",
       "      <td>[2145232497, 2336850026, 2671773241, 144628305...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14028714</th>\n",
       "      <td>2181038068</td>\n",
       "      <td>1</td>\n",
       "      <td>\"Giving machines humanlike eyes\"</td>\n",
       "      <td>2015</td>\n",
       "      <td>66124381</td>\n",
       "      <td>[2974799061, 2723812261, 2617766515]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14028715</th>\n",
       "      <td>2147483638</td>\n",
       "      <td>1</td>\n",
       "      <td>\"Effect of three anaesthetic techniques on is...</td>\n",
       "      <td>2004</td>\n",
       "      <td>33418846</td>\n",
       "      <td>[2308317210, 2070658732, 2496673173, 2930434804]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14028716</th>\n",
       "      <td>2315255800</td>\n",
       "      <td>1</td>\n",
       "      <td>\"Very high fluence collagen cross-linking as ...</td>\n",
       "      <td>2013</td>\n",
       "      <td>69264623</td>\n",
       "      <td>[2251267400]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14028717 rows × 6 columns</p>\n",
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      "text/plain": [
       "                  id  queryLevel  \\\n",
       "0         2038431747           0   \n",
       "1         1010827267           0   \n",
       "2         2411724805           0   \n",
       "3         2004877318           0   \n",
       "4         2025848838           0   \n",
       "...              ...         ...   \n",
       "14028712  2080374763           1   \n",
       "14028713  2147483632           1   \n",
       "14028714  2181038068           1   \n",
       "14028715  2147483638           1   \n",
       "14028716  2315255800           1   \n",
       "\n",
       "                                                      title  publicationYear  \\\n",
       "0          \"Topology learning solved by extended objects...             1994   \n",
       "1                 \"Multipotent Stem Cells In Murine Sclera\"             2011   \n",
       "2          \"Comparative study on results of prefrontal l...             1953   \n",
       "3          \"Ventral frontal deficits in psychopathy: Neu...             1995   \n",
       "4                     \"Iowa gambling task in Parkinson&#039             2011   \n",
       "...                                                     ...              ...   \n",
       "14028712   \"The spectral sensitivity of light-adapted ca...             1954   \n",
       "14028713   \"The 5-hydroxytryptamine (serotonin) receptor...             2013   \n",
       "14028714                   \"Giving machines humanlike eyes\"             2015   \n",
       "14028715   \"Effect of three anaesthetic techniques on is...             2004   \n",
       "14028716   \"Very high fluence collagen cross-linking as ...             2013   \n",
       "\n",
       "          idJournal                                            authors  \n",
       "0         207023548               [2163422316, 2419355596, 2289589767]  \n",
       "1          76304953  [2482935619, 2618323644, 2661080946, 266108094...  \n",
       "2         124451323               [2987759242, 2631162769, 2651065461]  \n",
       "3         165368631               [2572627602, 2278535667, 2800225512]  \n",
       "4          35508115                [237217217, 2129310074, 2647734675]  \n",
       "...             ...                                                ...  \n",
       "14028712    2090548                                       [2684787159]  \n",
       "14028713  136368880  [2145232497, 2336850026, 2671773241, 144628305...  \n",
       "14028714   66124381               [2974799061, 2723812261, 2617766515]  \n",
       "14028715   33418846   [2308317210, 2070658732, 2496673173, 2930434804]  \n",
       "14028716   69264623                                       [2251267400]  \n",
       "\n",
       "[14028717 rows x 6 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subdata = data[['id', 'queryLevel', ' title',' publicationDate', ' appearsInJournal', 'authors']].reset_index(drop=True)\n",
    "subdata.rename(columns = {' publicationDate': 'publicationYear', ' appearsInJournal': 'idJournal', ' title':'title'}, inplace=True)\n",
    "#subdata['title'] = subdata['title'].apply(lambda x: eval(x))\n",
    "subdata['publicationYear'].fillna('-1', inplace=True)\n",
    "subdata['publicationYear'] = [int(re.findall(r'\\d+', i)[0]) if i != '-1' else -1 for i in subdata['publicationYear']]\n",
    "subdata['idJournal'].fillna(-1, inplace=True)\n",
    "subdata['idJournal'] = [int(i) if i != -1 else -1 for i in subdata['idJournal']]\n",
    "subdata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb30452f",
   "metadata": {},
   "source": [
    "## 2.1 - Add journals' name and associated discipline from WoS classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7ac80929",
   "metadata": {},
   "outputs": [],
   "source": [
    "def wos_split_strings(list_str):\n",
    "    l = []\n",
    "    for i in list_str:\n",
    "        l += re.split(' \\| ', i) # change separators if needed\n",
    "    return l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "faa7a51a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>idJournal</th>\n",
       "      <th>Journal title</th>\n",
       "      <th>Web of Science Categories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>61661</td>\n",
       "      <td>[JOURNAL OF PROSTHODONTICS-IMPLANT ESTHETIC AN...</td>\n",
       "      <td>[Dentistry, Oral Surgery &amp; Medicine]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>81127</td>\n",
       "      <td>[EUROPEAN HEART JOURNAL SUPPLEMENTS]</td>\n",
       "      <td>[Cardiac &amp; Cardiovascular System]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>146344</td>\n",
       "      <td>[JOURNAL OF MARRIAGE AND FAMILY]</td>\n",
       "      <td>[Sociology, Family Studies]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>161885</td>\n",
       "      <td>[CHINESE STUDIES IN HISTORY]</td>\n",
       "      <td>[History, Asian Studies]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>182001</td>\n",
       "      <td>[GIFTED CHILD QUARTERLY]</td>\n",
       "      <td>[Psychology, Educational, Education, Special]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>17605</th>\n",
       "      <td>3035668652</td>\n",
       "      <td>[EUROPEAN RADIOLOGY EXPERIMENTAL]</td>\n",
       "      <td>[Radiology, Nuclear Medicine &amp; Medical Imaging]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>3035671286</td>\n",
       "      <td>[SATELLITE NAVIGATION]</td>\n",
       "      <td>[Engineering, Aerospace, Telecommunications, R...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17607</th>\n",
       "      <td>3035730399</td>\n",
       "      <td>[JOURNAL OF THE EGYPTIAN PUBLIC HEALTH ASSOCIA...</td>\n",
       "      <td>[Public, Environmental &amp; Occupational Health]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17608</th>\n",
       "      <td>3035751226</td>\n",
       "      <td>[JOURNAL OF PATIENT-REPORTED OUTCOMES]</td>\n",
       "      <td>[Health Care Sciences &amp; Services, Health Polic...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17609</th>\n",
       "      <td>3035763596</td>\n",
       "      <td>[EGYPTIAN JOURNAL OF BRONCHOLOGY]</td>\n",
       "      <td>[Respiratory System]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17610 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        idJournal                                      Journal title  \\\n",
       "0           61661  [JOURNAL OF PROSTHODONTICS-IMPLANT ESTHETIC AN...   \n",
       "1           81127               [EUROPEAN HEART JOURNAL SUPPLEMENTS]   \n",
       "2          146344                   [JOURNAL OF MARRIAGE AND FAMILY]   \n",
       "3          161885                       [CHINESE STUDIES IN HISTORY]   \n",
       "4          182001                           [GIFTED CHILD QUARTERLY]   \n",
       "...           ...                                                ...   \n",
       "17605  3035668652                  [EUROPEAN RADIOLOGY EXPERIMENTAL]   \n",
       "17606  3035671286                             [SATELLITE NAVIGATION]   \n",
       "17607  3035730399  [JOURNAL OF THE EGYPTIAN PUBLIC HEALTH ASSOCIA...   \n",
       "17608  3035751226             [JOURNAL OF PATIENT-REPORTED OUTCOMES]   \n",
       "17609  3035763596                  [EGYPTIAN JOURNAL OF BRONCHOLOGY]   \n",
       "\n",
       "                               Web of Science Categories  \n",
       "0                   [Dentistry, Oral Surgery & Medicine]  \n",
       "1                      [Cardiac & Cardiovascular System]  \n",
       "2                            [Sociology, Family Studies]  \n",
       "3                               [History, Asian Studies]  \n",
       "4          [Psychology, Educational, Education, Special]  \n",
       "...                                                  ...  \n",
       "17605    [Radiology, Nuclear Medicine & Medical Imaging]  \n",
       "17606  [Engineering, Aerospace, Telecommunications, R...  \n",
       "17607      [Public, Environmental & Occupational Health]  \n",
       "17608  [Health Care Sciences & Services, Health Polic...  \n",
       "17609                               [Respiratory System]  \n",
       "\n",
       "[17610 rows x 3 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read the WoS disciplinary classification and add it to the journals database\n",
    "JournalsDisciplines = pd.read_csv(f'{path_input}journals_MAG2WOS.csv', sep=';')\n",
    "JournalsDisciplines.rename(columns={'MAG_journal_Id': 'idJournal'}, inplace=True)\n",
    "JournalsDisciplines = JournalsDisciplines.groupby('idJournal').agg(lambda x: x.tolist()).reset_index()\n",
    "JournalsDisciplines['Web of Science Categories'] = [wos_split_strings(i) for i in JournalsDisciplines['Web of Science Categories']]\n",
    "JournalsDisciplines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "068b823b",
   "metadata": {},
   "outputs": [],
   "source": [
    "subdata = pd.merge(subdata, JournalsDisciplines, on='idJournal', how='left')\n",
    "subdata.rename(columns={'Journal title': 'journal_title', 'Web of Science Categories': 'wos_domains'}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a0fe458",
   "metadata": {},
   "source": [
    "## 2.2 - Add AI keywords and main classes if `isIA=1`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "24528e7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read intersection dataset neuro and IA\n",
    "with open(f'{path_input}Papers_neuroscience_and_AI.json', 'r') as f:\n",
    "    dataIA = json.load(f)\n",
    "dataIA = pd.DataFrame.from_dict(dataIA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "0a2ecbf5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>publicationDate</th>\n",
       "      <th>issueIdentifier</th>\n",
       "      <th>created</th>\n",
       "      <th>publisher</th>\n",
       "      <th>volume</th>\n",
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       "      <th>citationCount</th>\n",
       "      <th>doi</th>\n",
       "      <th>title</th>\n",
       "      <th>referenceCount</th>\n",
       "      <th>appearsInJournal</th>\n",
       "      <th>queryLevel</th>\n",
       "      <th>authors</th>\n",
       "      <th>bookTitle</th>\n",
       "      <th>appearsInConferenceSeries</th>\n",
       "      <th>appearsInConferenceInstance</th>\n",
       "      <th>abstract</th>\n",
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       "      <th>0</th>\n",
       "      <td>2982150161</td>\n",
       "      <td>\"2020-03-07\"</td>\n",
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       "      <td>\"2019-11-01\"</td>\n",
       "      <td>\"Elsevier\"</td>\n",
       "      <td>\"380\"</td>\n",
       "      <td>\"11\"</td>\n",
       "      <td>\"19\"</td>\n",
       "      <td>\"1\"</td>\n",
       "      <td>10.1016/J.NEUCOM.2019.10.001</td>\n",
       "      <td>\"Delay tolerant containment control for secon...</td>\n",
       "      <td>\"32\"</td>\n",
       "      <td>45693802</td>\n",
       "      <td>0</td>\n",
       "      <td>[2698853177, 2894619631, 2090493536, 2980724061]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Abstract In this paper, a delay tolerant cont...</td>\n",
       "      <td>[convex hull, multi-agent system]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017460423</td>\n",
       "      <td>\"2004-12-01\"</td>\n",
       "      <td>\"4\"</td>\n",
       "      <td>\"2016-06-24\"</td>\n",
       "      <td>\"Lippincott Williams and Wilkins\"</td>\n",
       "      <td>\"14\"</td>\n",
       "      <td>\"187\"</td>\n",
       "      <td>\"189\"</td>\n",
       "      <td>\"0\"</td>\n",
       "      <td>10.1097/00041444-200412000-00003</td>\n",
       "      <td>\"Evaluation of genetic substructure in the Ir...</td>\n",
       "      <td>\"0\"</td>\n",
       "      <td>41645954</td>\n",
       "      <td>0</td>\n",
       "      <td>[2494464687, 2120665394, 2091809267, 2614997734]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>: The presence of genetic substructure has th...</td>\n",
       "      <td>[markov chain monte carlo]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2592080110</td>\n",
       "      <td>\"2017-03-03\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"2017-03-16\"</td>\n",
       "      <td>\"Dove Press\"</td>\n",
       "      <td>\"9\"</td>\n",
       "      <td>\"39\"</td>\n",
       "      <td>\"57\"</td>\n",
       "      <td>\"36\"</td>\n",
       "      <td>10.2147/NSS.S103467</td>\n",
       "      <td>\"New developments in the management of narcol...</td>\n",
       "      <td>\"0\"</td>\n",
       "      <td>2739268901</td>\n",
       "      <td>0</td>\n",
       "      <td>[2162133358, 2136137024]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>: Narcolepsy is a life-long, underrecognized ...</td>\n",
       "      <td>[mycin]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2613051638</td>\n",
       "      <td>\"2017-07-01\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"2017-05-19\"</td>\n",
       "      <td>\"Elsevier\"</td>\n",
       "      <td>\"158\"</td>\n",
       "      <td>\"7\"</td>\n",
       "      <td>\"13\"</td>\n",
       "      <td>\"0\"</td>\n",
       "      <td>10.1016/J.PBB.2017.05.004</td>\n",
       "      <td>\"Posterior hypothalamus glutamate infusion de...</td>\n",
       "      <td>\"51\"</td>\n",
       "      <td>59685179</td>\n",
       "      <td>0</td>\n",
       "      <td>[2049946654, 2108670995, 2944770929, 2667938519]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Abstract Objectives Seizures are epileptic ma...</td>\n",
       "      <td>[lenet]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2617245985</td>\n",
       "      <td>\"2017-11-29\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"2017-06-05\"</td>\n",
       "      <td>\"Elsevier\"</td>\n",
       "      <td>\"266\"</td>\n",
       "      <td>\"325\"</td>\n",
       "      <td>\"335\"</td>\n",
       "      <td>\"15\"</td>\n",
       "      <td>10.1016/J.NEUCOM.2017.05.042</td>\n",
       "      <td>\"Deep learning algorithms for discriminant au...</td>\n",
       "      <td>\"36\"</td>\n",
       "      <td>45693802</td>\n",
       "      <td>0</td>\n",
       "      <td>[3024761849, 2619442247]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>In this paper, a new family of Autoencoders (...</td>\n",
       "      <td>[autoencoder, dimensionality reduction]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>614353</th>\n",
       "      <td>1979711143</td>\n",
       "      <td>\"1998-07-24\"</td>\n",
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       "      <td>\"2016-06-24\"</td>\n",
       "      <td>\"ACM\"</td>\n",
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       "      <td>\"209\"</td>\n",
       "      <td>\"217\"</td>\n",
       "      <td>\"937\"</td>\n",
       "      <td>10.1145/279943.279985</td>\n",
       "      <td>\"Large margin classification using the percep...</td>\n",
       "      <td>\"21\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>[2985572601, 2626311920]</td>\n",
       "      <td>\"COLT\"</td>\n",
       "      <td>1177622950</td>\n",
       "      <td>2786194060</td>\n",
       "      <td>We introduce and analyze a new algorithm for ...</td>\n",
       "      <td>[classifier]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614354</th>\n",
       "      <td>2147483361</td>\n",
       "      <td>\"2011-04-01\"</td>\n",
       "      <td>\"4\"</td>\n",
       "      <td>\"2016-06-24\"</td>\n",
       "      <td>\"IEEE\"</td>\n",
       "      <td>\"33\"</td>\n",
       "      <td>\"754\"</td>\n",
       "      <td>\"766\"</td>\n",
       "      <td>\"137\"</td>\n",
       "      <td>10.1109/TPAMI.2010.133</td>\n",
       "      <td>\"Harvesting Image Databases from the Web\"</td>\n",
       "      <td>\"26\"</td>\n",
       "      <td>199944782</td>\n",
       "      <td>1</td>\n",
       "      <td>[2985357453, 1966707555, 2469405535]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>The objective of this work is to automaticall...</td>\n",
       "      <td>[training data, classifier]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614355</th>\n",
       "      <td>2919235374</td>\n",
       "      <td>\"2019-05-01\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"2019-03-11\"</td>\n",
       "      <td>\"Academic Press\"</td>\n",
       "      <td>\"61\"</td>\n",
       "      <td>\"42\"</td>\n",
       "      <td>\"49\"</td>\n",
       "      <td>\"2\"</td>\n",
       "      <td>10.1016/J.JVCIR.2019.03.004</td>\n",
       "      <td>\"Application of artificial intelligence algor...</td>\n",
       "      <td>\"24\"</td>\n",
       "      <td>105424869</td>\n",
       "      <td>1</td>\n",
       "      <td>[2919870832, 2919889170]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Abstract As the main media of human communica...</td>\n",
       "      <td>[artificial intelligence, combinatorial optimi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614356</th>\n",
       "      <td>2046820148</td>\n",
       "      <td>\"2014-03-20\"</td>\n",
       "      <td>\"3\"</td>\n",
       "      <td>\"2016-06-24\"</td>\n",
       "      <td>\"Public Library of Science\"</td>\n",
       "      <td>\"9\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"52\"</td>\n",
       "      <td>10.1371/JOURNAL.PONE.0091969</td>\n",
       "      <td>\"Visceral adiposity index (VAI) is predictive...</td>\n",
       "      <td>\"41\"</td>\n",
       "      <td>202381698</td>\n",
       "      <td>1</td>\n",
       "      <td>[45745419, 1900658159, 3005837255, 2080409075,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aims Although there is still no clear definit...</td>\n",
       "      <td>[cluster analysis]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614357</th>\n",
       "      <td>2080374594</td>\n",
       "      <td>\"2004-05-01\"</td>\n",
       "      <td>\"1\"</td>\n",
       "      <td>\"2016-06-24\"</td>\n",
       "      <td>\"Blackwell Publishing Ltd\"</td>\n",
       "      <td>\"234\"</td>\n",
       "      <td>\"43\"</td>\n",
       "      <td>\"49\"</td>\n",
       "      <td>\"97\"</td>\n",
       "      <td>10.1111/J.1574-6968.2004.TB09511.X</td>\n",
       "      <td>\"Molecular characterization and analysis of t...</td>\n",
       "      <td>\"26\"</td>\n",
       "      <td>34954451</td>\n",
       "      <td>1</td>\n",
       "      <td>[2788874313, 1980174942, 2559322472]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bacillus subtilis AU195 produces bacillomycin...</td>\n",
       "      <td>[mycin]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>614358 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                id  publicationDate  issueIdentifier        created  \\\n",
       "0       2982150161     \"2020-03-07\"              NaN   \"2019-11-01\"   \n",
       "1       2017460423     \"2004-12-01\"              \"4\"   \"2016-06-24\"   \n",
       "2       2592080110     \"2017-03-03\"              NaN   \"2017-03-16\"   \n",
       "3       2613051638     \"2017-07-01\"              NaN   \"2017-05-19\"   \n",
       "4       2617245985     \"2017-11-29\"              NaN   \"2017-06-05\"   \n",
       "...            ...              ...              ...            ...   \n",
       "614353  1979711143     \"1998-07-24\"              \"3\"   \"2016-06-24\"   \n",
       "614354  2147483361     \"2011-04-01\"              \"4\"   \"2016-06-24\"   \n",
       "614355  2919235374     \"2019-05-01\"              NaN   \"2019-03-11\"   \n",
       "614356  2046820148     \"2014-03-20\"              \"3\"   \"2016-06-24\"   \n",
       "614357  2080374594     \"2004-05-01\"              \"1\"   \"2016-06-24\"   \n",
       "\n",
       "                                 publisher  volume  startingPage  endingPage  \\\n",
       "0                               \"Elsevier\"   \"380\"          \"11\"        \"19\"   \n",
       "1        \"Lippincott Williams and Wilkins\"    \"14\"         \"187\"       \"189\"   \n",
       "2                             \"Dove Press\"     \"9\"          \"39\"        \"57\"   \n",
       "3                               \"Elsevier\"   \"158\"           \"7\"        \"13\"   \n",
       "4                               \"Elsevier\"   \"266\"         \"325\"       \"335\"   \n",
       "...                                    ...     ...           ...         ...   \n",
       "614353                               \"ACM\"    \"37\"         \"209\"       \"217\"   \n",
       "614354                              \"IEEE\"    \"33\"         \"754\"       \"766\"   \n",
       "614355                    \"Academic Press\"    \"61\"          \"42\"        \"49\"   \n",
       "614356         \"Public Library of Science\"     \"9\"           NaN         NaN   \n",
       "614357          \"Blackwell Publishing Ltd\"   \"234\"          \"43\"        \"49\"   \n",
       "\n",
       "        citationCount                                 doi  \\\n",
       "0                 \"1\"        10.1016/J.NEUCOM.2019.10.001   \n",
       "1                 \"0\"    10.1097/00041444-200412000-00003   \n",
       "2                \"36\"                 10.2147/NSS.S103467   \n",
       "3                 \"0\"           10.1016/J.PBB.2017.05.004   \n",
       "4                \"15\"        10.1016/J.NEUCOM.2017.05.042   \n",
       "...               ...                                 ...   \n",
       "614353          \"937\"               10.1145/279943.279985   \n",
       "614354          \"137\"              10.1109/TPAMI.2010.133   \n",
       "614355            \"2\"         10.1016/J.JVCIR.2019.03.004   \n",
       "614356           \"52\"        10.1371/JOURNAL.PONE.0091969   \n",
       "614357           \"97\"  10.1111/J.1574-6968.2004.TB09511.X   \n",
       "\n",
       "                                                    title  referenceCount  \\\n",
       "0        \"Delay tolerant containment control for secon...            \"32\"   \n",
       "1        \"Evaluation of genetic substructure in the Ir...             \"0\"   \n",
       "2        \"New developments in the management of narcol...             \"0\"   \n",
       "3        \"Posterior hypothalamus glutamate infusion de...            \"51\"   \n",
       "4        \"Deep learning algorithms for discriminant au...            \"36\"   \n",
       "...                                                   ...             ...   \n",
       "614353   \"Large margin classification using the percep...            \"21\"   \n",
       "614354          \"Harvesting Image Databases from the Web\"            \"26\"   \n",
       "614355   \"Application of artificial intelligence algor...            \"24\"   \n",
       "614356   \"Visceral adiposity index (VAI) is predictive...            \"41\"   \n",
       "614357   \"Molecular characterization and analysis of t...            \"26\"   \n",
       "\n",
       "        appearsInJournal  queryLevel  \\\n",
       "0               45693802           0   \n",
       "1               41645954           0   \n",
       "2             2739268901           0   \n",
       "3               59685179           0   \n",
       "4               45693802           0   \n",
       "...                  ...         ...   \n",
       "614353               NaN           1   \n",
       "614354         199944782           1   \n",
       "614355         105424869           1   \n",
       "614356         202381698           1   \n",
       "614357          34954451           1   \n",
       "\n",
       "                                                  authors  bookTitle  \\\n",
       "0        [2698853177, 2894619631, 2090493536, 2980724061]        NaN   \n",
       "1        [2494464687, 2120665394, 2091809267, 2614997734]        NaN   \n",
       "2                                [2162133358, 2136137024]        NaN   \n",
       "3        [2049946654, 2108670995, 2944770929, 2667938519]        NaN   \n",
       "4                                [3024761849, 2619442247]        NaN   \n",
       "...                                                   ...        ...   \n",
       "614353                           [2985572601, 2626311920]     \"COLT\"   \n",
       "614354               [2985357453, 1966707555, 2469405535]        NaN   \n",
       "614355                           [2919870832, 2919889170]        NaN   \n",
       "614356  [45745419, 1900658159, 3005837255, 2080409075,...        NaN   \n",
       "614357               [2788874313, 1980174942, 2559322472]        NaN   \n",
       "\n",
       "        appearsInConferenceSeries  appearsInConferenceInstance  \\\n",
       "0                             NaN                          NaN   \n",
       "1                             NaN                          NaN   \n",
       "2                             NaN                          NaN   \n",
       "3                             NaN                          NaN   \n",
       "4                             NaN                          NaN   \n",
       "...                           ...                          ...   \n",
       "614353                 1177622950                   2786194060   \n",
       "614354                        NaN                          NaN   \n",
       "614355                        NaN                          NaN   \n",
       "614356                        NaN                          NaN   \n",
       "614357                        NaN                          NaN   \n",
       "\n",
       "                                                 abstract  \\\n",
       "0        Abstract In this paper, a delay tolerant cont...   \n",
       "1        : The presence of genetic substructure has th...   \n",
       "2        : Narcolepsy is a life-long, underrecognized ...   \n",
       "3        Abstract Objectives Seizures are epileptic ma...   \n",
       "4        In this paper, a new family of Autoencoders (...   \n",
       "...                                                   ...   \n",
       "614353   We introduce and analyze a new algorithm for ...   \n",
       "614354   The objective of this work is to automaticall...   \n",
       "614355   Abstract As the main media of human communica...   \n",
       "614356   Aims Although there is still no clear definit...   \n",
       "614357   Bacillus subtilis AU195 produces bacillomycin...   \n",
       "\n",
       "                                               keywordsIA  \n",
       "0                       [convex hull, multi-agent system]  \n",
       "1                              [markov chain monte carlo]  \n",
       "2                                                 [mycin]  \n",
       "3                                                 [lenet]  \n",
       "4                 [autoencoder, dimensionality reduction]  \n",
       "...                                                   ...  \n",
       "614353                                       [classifier]  \n",
       "614354                        [training data, classifier]  \n",
       "614355  [artificial intelligence, combinatorial optimi...  \n",
       "614356                                 [cluster analysis]  \n",
       "614357                                            [mycin]  \n",
       "\n",
       "[614358 rows x 20 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataIA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "cae0f0a7",
   "metadata": {},
   "outputs": [
    {
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       "      <th>keywords</th>\n",
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       "      <td>simulated annealing</td>\n",
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       "      <th>2</th>\n",
       "      <td>hierarchical clustering</td>\n",
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       "      <th>3</th>\n",
       "      <td>principal component analysis</td>\n",
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       "      <th>4</th>\n",
       "      <td>unsupervised learning</td>\n",
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       "      <th>...</th>\n",
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       "      <td>fast and frugal trees</td>\n",
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       "      <td>chess playing computer</td>\n",
       "      <td>robotics</td>\n",
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       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>bias variance tradeoff</td>\n",
       "      <td>optimization</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>convolutional neural network (cnn)</td>\n",
       "      <td>deep learning</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>532 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               keywords             class\n",
       "0                evolutionary algorithm      optimization\n",
       "1                   simulated annealing      optimization\n",
       "2               hierarchical clustering  cluster analysis\n",
       "3          principal component analysis  cluster analysis\n",
       "4                 unsupervised learning  machine learning\n",
       "..                                  ...               ...\n",
       "527            multi swarm optimization      optimization\n",
       "528               fast and frugal trees        AI general\n",
       "529              chess playing computer          robotics\n",
       "530              bias variance tradeoff      optimization\n",
       "531  convolutional neural network (cnn)     deep learning\n",
       "\n",
       "[532 rows x 2 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read the classification of AI keywords\n",
    "classification = pd.read_csv(f'{path_input}AI_kw_classification.txt', sep=';', names=['keywords', 'class'])\n",
    "classification['keywords'] = [i.strip() for i in classification['keywords']]\n",
    "classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6b630677",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataIA['classIA'] = (dataIA['keywordsIA'].apply(\n",
    "    lambda x: list(set([classification[classification['keywords']==k]['class'].values[0] for k in x if len(classification[classification['keywords']==k]['class'].values) != 0]))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5f83a1bc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>queryLevel</th>\n",
       "      <th>title</th>\n",
       "      <th>publicationYear</th>\n",
       "      <th>idJournal</th>\n",
       "      <th>authors</th>\n",
       "      <th>isIA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2038431747</td>\n",
       "      <td>0</td>\n",
       "      <td>\"Topology learning solved by extended objects...</td>\n",
       "      <td>1994</td>\n",
       "      <td>207023548</td>\n",
       "      <td>[2163422316, 2419355596, 2289589767]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1010827267</td>\n",
       "      <td>0</td>\n",
       "      <td>\"Multipotent Stem Cells In Murine Sclera\"</td>\n",
       "      <td>2011</td>\n",
       "      <td>76304953</td>\n",
       "      <td>[2482935619, 2618323644, 2661080946, 266108094...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2411724805</td>\n",
       "      <td>0</td>\n",
       "      <td>\"Comparative study on results of prefrontal l...</td>\n",
       "      <td>1953</td>\n",
       "      <td>124451323</td>\n",
       "      <td>[2987759242, 2631162769, 2651065461]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2004877318</td>\n",
       "      <td>0</td>\n",
       "      <td>\"Ventral frontal deficits in psychopathy: Neu...</td>\n",
       "      <td>1995</td>\n",
       "      <td>165368631</td>\n",
       "      <td>[2572627602, 2278535667, 2800225512]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2025848838</td>\n",
       "      <td>0</td>\n",
       "      <td>\"Iowa gambling task in Parkinson&amp;#039</td>\n",
       "      <td>2011</td>\n",
       "      <td>35508115</td>\n",
       "      <td>[237217217, 2129310074, 2647734675]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>14028712</th>\n",
       "      <td>2080374763</td>\n",
       "      <td>1</td>\n",
       "      <td>\"The spectral sensitivity of light-adapted ca...</td>\n",
       "      <td>1954</td>\n",
       "      <td>2090548</td>\n",
       "      <td>[2684787159]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14028713</th>\n",
       "      <td>2147483632</td>\n",
       "      <td>1</td>\n",
       "      <td>\"The 5-hydroxytryptamine (serotonin) receptor...</td>\n",
       "      <td>2013</td>\n",
       "      <td>136368880</td>\n",
       "      <td>[2145232497, 2336850026, 2671773241, 144628305...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14028714</th>\n",
       "      <td>2181038068</td>\n",
       "      <td>1</td>\n",
       "      <td>\"Giving machines humanlike eyes\"</td>\n",
       "      <td>2015</td>\n",
       "      <td>66124381</td>\n",
       "      <td>[2974799061, 2723812261, 2617766515]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14028715</th>\n",
       "      <td>2147483638</td>\n",
       "      <td>1</td>\n",
       "      <td>\"Effect of three anaesthetic techniques on is...</td>\n",
       "      <td>2004</td>\n",
       "      <td>33418846</td>\n",
       "      <td>[2308317210, 2070658732, 2496673173, 2930434804]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14028716</th>\n",
       "      <td>2315255800</td>\n",
       "      <td>1</td>\n",
       "      <td>\"Very high fluence collagen cross-linking as ...</td>\n",
       "      <td>2013</td>\n",
       "      <td>69264623</td>\n",
       "      <td>[2251267400]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14028717 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id  queryLevel  \\\n",
       "0         2038431747           0   \n",
       "1         1010827267           0   \n",
       "2         2411724805           0   \n",
       "3         2004877318           0   \n",
       "4         2025848838           0   \n",
       "...              ...         ...   \n",
       "14028712  2080374763           1   \n",
       "14028713  2147483632           1   \n",
       "14028714  2181038068           1   \n",
       "14028715  2147483638           1   \n",
       "14028716  2315255800           1   \n",
       "\n",
       "                                                      title  publicationYear  \\\n",
       "0          \"Topology learning solved by extended objects...             1994   \n",
       "1                 \"Multipotent Stem Cells In Murine Sclera\"             2011   \n",
       "2          \"Comparative study on results of prefrontal l...             1953   \n",
       "3          \"Ventral frontal deficits in psychopathy: Neu...             1995   \n",
       "4                     \"Iowa gambling task in Parkinson&#039             2011   \n",
       "...                                                     ...              ...   \n",
       "14028712   \"The spectral sensitivity of light-adapted ca...             1954   \n",
       "14028713   \"The 5-hydroxytryptamine (serotonin) receptor...             2013   \n",
       "14028714                   \"Giving machines humanlike eyes\"             2015   \n",
       "14028715   \"Effect of three anaesthetic techniques on is...             2004   \n",
       "14028716   \"Very high fluence collagen cross-linking as ...             2013   \n",
       "\n",
       "          idJournal                                            authors  isIA  \n",
       "0         207023548               [2163422316, 2419355596, 2289589767]     0  \n",
       "1          76304953  [2482935619, 2618323644, 2661080946, 266108094...     0  \n",
       "2         124451323               [2987759242, 2631162769, 2651065461]     0  \n",
       "3         165368631               [2572627602, 2278535667, 2800225512]     0  \n",
       "4          35508115                [237217217, 2129310074, 2647734675]     0  \n",
       "...             ...                                                ...   ...  \n",
       "14028712    2090548                                       [2684787159]     0  \n",
       "14028713  136368880  [2145232497, 2336850026, 2671773241, 144628305...     0  \n",
       "14028714   66124381               [2974799061, 2723812261, 2617766515]     0  \n",
       "14028715   33418846   [2308317210, 2070658732, 2496673173, 2930434804]     0  \n",
       "14028716   69264623                                       [2251267400]     0  \n",
       "\n",
       "[14028717 rows x 7 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subdata['isIA'] = np.where(subdata['id'].isin(dataIA['id']), 1, 0)\n",
    "subdata = pd.merge(subdata, dataIA[['id', 'keywordsIA', 'classIA']], on='id', how='left').fillna(-1)\n",
    "subdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "97d97258",
   "metadata": {},
   "outputs": [],
   "source": [
    "del dataIA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6aa589e0",
   "metadata": {},
   "source": [
    "## 2.3 - Save the final subdataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "921b6a71",
   "metadata": {},
   "outputs": [],
   "source": [
    "subdata.to_csv(f\"{path_output}citationNetwork_nodes.csv\", sep=';', index=False)\n",
    "# del subdata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e1846a4",
   "metadata": {},
   "source": [
    "With `queryLevel=0`, we have 2.139.745 papers, including 2.052.292 papers published between 1970 and 2020, disseminated in 479 journals."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "760e2bb4",
   "metadata": {},
   "source": [
    "# 3 - Build filtered egocentric datasets for $\\mathcal{P}\\cap AI$ and $\\mathcal{P}\\cap \\overline{AI}$\n",
    "It includes papers with main attributes:\n",
    "- `queryLevel=0`\n",
    "- published after 1970\n",
    "- cited at least 10 times, ie. $k_{in}\\geqslant10$\n",
    "- citing at least 10 other references, ie. $k_{out}\\geqslant10$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98294a12",
   "metadata": {},
   "source": [
    "## 3.1 - $\\mathcal{P}\\cap \\overline{AI}$\n",
    "\n",
    "Here `isIA=0`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4b3ad3f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.fillna(-1, inplace=True)\n",
    "data[' referenceCount'] = data[' referenceCount'].apply(lambda x: int(eval(x)) if x!=-1 else -1)\n",
    "data[' citationCount'] = data[' citationCount'].apply(lambda x: int(eval(x)) if x!=-1 else -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "32ebca95",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get only the ids of the papers that counts at least 10 reference and citation\n",
    "ids_nbRefSup10nbImpSup10 = data[(data[' referenceCount'] >= 10)\n",
    "                                & (data[' citationCount'] >= 10)]['id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7c6317fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "subdata_subset = subdata[(subdata['queryLevel']==0) \n",
    "                    & (subdata['isIA']==0) \n",
    "                    & (subdata['publicationYear']>=1970) # & (subdata['publicationYear']<2020) \n",
    "                    & (subdata['id'].isin(ids_nbRefSup10nbImpSup10))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9dbea894",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "829317"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(subdata_subset) # here 829317"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e0182004",
   "metadata": {},
   "outputs": [],
   "source": [
    "subdata_subset.to_csv(f\"{path_output}papersNeuro_minRef10_minImp10_from1970.csv\", sep=';', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b0f007fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# force to free memory\n",
    "del data, CN, nodes\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dfe6a7f",
   "metadata": {},
   "source": [
    "## 3.2 - $\\mathcal{P}\\cap AI$\n",
    "\n",
    "Here `isIA=1`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "535e9e5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "ids_nbRefSup10nbImpSup10 = data[(data[' referenceCount'] >= 10)\n",
    "                                & (data[' citationCount'] >= 10)]['id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6c2cd3b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "subdata_subset = subdata[(subdata['queryLevel']==0) \n",
    "                    & (subdata['isIA']==1) \n",
    "                    & (subdata['publicationYear']>=1970) # & (subdata['publicationYear']<2020) \n",
    "                    & (subdata['id'].isin(ids_nbRefSup10nbImpSup10))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0f604626",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26374"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(subdata_subset) # here 26374"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4dddd5df",
   "metadata": {},
   "outputs": [],
   "source": [
    "subdata_subset.to_csv(f\"{path_output}papersNeuroAI_minRef10_minImp10_from1970.csv\", sep=';', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b875d8da",
   "metadata": {},
   "source": [
    "## 3.3 - Compare the structure of the original citation network with the one produced by the filtered datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ec5dde18",
   "metadata": {},
   "outputs": [],
   "source": [
    "path_output = r'/home/sylvain/DOCTORAT/DATA/microsoft_academic/corpus/neuroscience/database/updated/QL0/filtered_data_for_analyzes/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2b3d3be9",
   "metadata": {},
   "outputs": [],
   "source": [
    "links = pd.read_csv(f'{path_input}citationNetwork.csv', sep=',', usecols=['citing', 'cited']).drop_duplicates()\n",
    "nodes = pd.read_csv(f'{path_input}citationNetwork_nodes.csv', sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ac8fb45f",
   "metadata": {},
   "outputs": [],
   "source": [
    "nodes_Ql0 = nodes[(nodes['queryLevel']==0) & (nodes['publicationYear']>=1970) & (nodes['queryLevel']<=2020)]\n",
    "links_Ql0 = links[(links['cited'].isin(nodes_Ql0['id']))|(links['citing'].isin(nodes_Ql0['id']))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9db904a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2071"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del links\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "14e48379",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13289110"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(links_Ql0['citing'])|set(links_Ql0['cited']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "63e3b45b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11236818"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "13289110-len(nodes_Ql0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db037785",
   "metadata": {},
   "outputs": [],
   "source": [
    "CN_tot = nx.from_pandas_edgelist(links_Ql0, source='citing', target='cited', edge_attr=None, create_using=nx.DiGraph, edge_key=None)\n",
    "degree_freq = nx.degree_histogram(CN_tot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b245f367",
   "metadata": {},
   "outputs": [],
   "source": [
    "nodes_Ql0_AI = nodes_Ql0[nodes_Ql0['isIA']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1c139d0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>121637856</th>\n",
       "      <td>3028593627</td>\n",
       "      <td>2031132539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121637859</th>\n",
       "      <td>3028593627</td>\n",
       "      <td>2235196730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121637860</th>\n",
       "      <td>3028593627</td>\n",
       "      <td>2775079417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3805467 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               citing       cited\n",
       "446            233906  2108694113\n",
       "449            233906  2165707697\n",
       "491            296994  2065461085\n",
       "609            355896  2059586821\n",
       "734            414044  2066700516\n",
       "...               ...         ...\n",
       "121637710  3028587596  1988115241\n",
       "121637759  3028590342  2769014061\n",
       "121637856  3028593627  2031132539\n",
       "121637859  3028593627  2235196730\n",
       "121637860  3028593627  2775079417\n",
       "\n",
       "[3805467 rows x 2 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "links_Ql0[links_Ql0['cited'].isin(nodes_Ql0_AI['id']) | links_Ql0['citing'].isin(nodes_Ql0_AI['id'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fd21873c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>citing</th>\n",
       "      <th>cited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>233906</td>\n",
       "      <td>2108694113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>233906</td>\n",
       "      <td>2165707697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>296994</td>\n",
       "      <td>2065461085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>355896</td>\n",
       "      <td>2059586821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>734</th>\n",
       "      <td>414044</td>\n",
       "      <td>2066700516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121637786</th>\n",
       "      <td>3028591432</td>\n",
       "      <td>2053763916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121637795</th>\n",
       "      <td>3028591432</td>\n",
       "      <td>2086539848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121637801</th>\n",
       "      <td>3028591432</td>\n",
       "      <td>2101863328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121637824</th>\n",
       "      <td>3028591432</td>\n",
       "      <td>2166709585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121637827</th>\n",
       "      <td>3028591432</td>\n",
       "      <td>2552833581</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1099174 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               citing       cited\n",
       "446            233906  2108694113\n",
       "449            233906  2165707697\n",
       "491            296994  2065461085\n",
       "609            355896  2059586821\n",
       "734            414044  2066700516\n",
       "...               ...         ...\n",
       "121637786  3028591432  2053763916\n",
       "121637795  3028591432  2086539848\n",
       "121637801  3028591432  2101863328\n",
       "121637824  3028591432  2166709585\n",
       "121637827  3028591432  2552833581\n",
       "\n",
       "[1099174 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "links_Ql0[links_Ql0['cited'].isin(nodes_Ql0_AI['id']) & links_Ql0['citing'].isin(nodes_Ql0['id'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "35a0011d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "nodes_filtered_AI = pd.read_csv(f'{path_output}papersNeuroAI_minRef10_minImp10_from1970.csv', sep=';')\n",
    "nodes_filtered_nonAI =  pd.read_csv(f'{path_output}papersNeuro_minRef10_minImp10_from1970.csv', sep=';')\n",
    "nodes_filtered = pd.concat([nodes_filtered_AI,nodes_filtered_nonAI])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2992e5ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "with open(f'{path_input}citation_net_degreeHisto.txt', 'w') as f:\n",
    "    for d in degree_freq:\n",
    "        f.write(str(d)+'\\n')\n",
    "with open(f'{path_input}citation_net_degreeHisto.txt', 'r') as f:\n",
    "    degree_freq = [int(g[:-1]) for g in f.readlines()]\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6c12a974",
   "metadata": {},
   "outputs": [],
   "source": [
    "links_filtered = links_Ql0[(links_Ql0['cited'].isin(nodes_filtered['id']))\n",
    "                           |(links_Ql0['citing'].isin(nodes_filtered['id']))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "074945f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "CN_filtered = nx.from_pandas_edgelist(links_filtered, source='citing', target='cited', edge_attr=None, create_using=nx.DiGraph, edge_key=None)\n",
    "degree_freq_filtered = nx.degree_histogram(CN_filtered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f87fd5e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def degree_distrib_from_df(df):\n",
    "    nodes = set(df['citing'])|set(df['cited'])\n",
    "    d = [len(df[(df['citing']==i)|(df['cited']==i)]) for i in tqdm(nodes)]\n",
    "    return d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "bf9de38e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rVe5eMMHdRxmKpP5ZD0UsweeOcv6aJXlu1cTa29sT13N8Ph8+ny/twonW1lZ27NgBRIs1ACoqKqitrU3s09zcnBiO27FjB3V1dfj9ftrb26mvrycYDNLW1kYgEKCtrY26urrEjRXHOja57cn3vXLKdbJMOLqYotBUTJEfg+EIux45yM6HX53SeWzsnsOHXBH2eocSq7WPxQDXvH0xjdWXMK/cO6XnlvTseOgA27ceGHP72g8sZ9X1y/PYotFGFlOEQiGuvvrqRKXfSKkKJ9J9DKCxMXpP13jlIESDbNeuXfh8PkKhEMuXL2fz5s1UV1fT2NiYuFY20bGBQICGhoZhw46BQCBREFIsxRQKqnEoqPLr9VeOsfXf9zA4MART+Jbb2MFDwDbvAM/OGj+wABbN8VL19sXUX/c2hVYO7d9+mF9+/+WUt4vxlLq46vYLCt6jam5upq6ubljJeWtr65jvA1MJqlAoxPz580c9XldXx8qVKxO9LmMMu3btGnbn33SOraiooKWlZdQdg4ut6s9xQ38yc5153nzu+uIVBHcf5Xcvd7Hv2cPYSVyyMLHFm1zAu8Ie3hn2TNjLOnoyzA+2H+IH2w+xZtl8Nv/FKgVWDvhXLOLJH+1Pua3EZfCvWJTnFqXm8/loaGhIfB3vuWRbIBBI9HySdXV1jerBjQybiY4NBoMEg8Givq19nIJKHMXtdXH+miWcv2YJV952wZSHBE0stpZGXCztdXFtr4dnx7iWFbf94DEu/UwbZ/nK+Pj7zuWmyrfqWlaWuD0ubrznsmE34vSUuihxGW685zLcDv0+5+rNvquriwULFiSuN8WN/HoyxzqpHH6qFFTiWG6vi7UfqODsCxfw0399jqEpFIQl97Le3e/hXf0eTpghDrotv5o1MGwx3Ljfh/rY+OALbHzwBdZdeAb/UnOpellZsHjZXO74/OUEdx/lxJu9zD19Fv4VixwbUpBecGQiXpzh9/szriCMm+jYYiyaGIvjytNFRjrzvPms//J7WXX9OZRkMFl4LAZDCYZ5toRLB1zcc6KMvz5eSlW3B+8Y6+gGXnqDSz/Txq1ff1pzs7Ig3nNedf1yzl+zxNEhlQvBYBCfz5cIwOTy97j29vFn20x0bGVlZaI6caTJzAMrJAWVFIV472r9l6+k6s63c+HlS6b86jVJH3OsSYTWx0OlXDNGaMWHBe99YLfmZElG/H5/ytDYvHlzomw9Ln79CcYPlYmOHWt7sVHV3zhU9eds8bL2XY+8is38no1jskklhwNYOksse0ojvJiiEONbH13N+y48I3tPLgXV2NjIli1bEovSVlRUDCv9ThYMBmloaKC5uZna2loaGhro6uoa9lhdXV3i+lZ7eztbtmxh9erV+P3+Yde94vOjVq9ejc/nSwwLtre3s2nTJlpbW6mtraWmpmbUMORYxyZvj/ewQqEQlZWVrFy5klWrVlFfX5/1YU3HlacbY+YSXWPPD/iAhUAn0TvshoCgtfbgVBtZKAqq4hC/B9bzvzzEm691Z/XcyaFlgSNmiB/PCdObNFL1lnml/PTuyzlj7qysPrdIMXJEUMXCqS72uRxoB+Iz+OJX9hYQDS4/MJ/oChJN1tonpt7k/FFQFZ++k2GeuH8fB/a8mZPzx4PrOJbvndY/LLDee/5Cmj6yWhWCMqMVNKhiAfWfRMOpCXjcWjv2FPPhx14N1ABXA/XW2p9Mrrn5paAqXrkaFoyLB9ZJLL+eNZgYFjTA9+5ay+XnnZ79JxUpAgULKmPMLcCtwOettc9N6QmNWQ+sA9Zbax29XLKCqvjlclgQhq+C8UB5P7/zRr/+xxsu5K4rpk95sEi6ChJUxphPAB3Z7AUZY+YBDUSD72C2zpttCqrppe9kmGf+K8iB59+g98Rg1s9vsex1DfLYnEEiBj5RdS53X31B1p9HxMnyHlTGmBWxJ5xSL2qc86+31m7OxbmzQUE1feUqtOIL426J9a4+vPqtbLrl0qydX8TpHFFMMZMoqGaGvpNhfv3jDjqeO8JAX3YuaFksT3jCtM8e4u1vOY2ffOxyFVnIjODIoDLG3Ay0O3kIb7IUVDNPPLQOPP8G/d1Tm9BrsbzBED+aGyZcAt9XkYXMAI4LKmPM14kWWXRaa88bsW090fL0pmINMQXVzDYYjrDv2T+w8+EDdIcGJnWOeLFFW2mYPbOGVGQh054Tg+oTROdRVVprvzDGPuuJBllRlKQnU1BJXN/JMA9+6Tm6Xp9c5aDF8pQ7zLY5QyqykGnNkUE1VkCN2G+TtXbjpJ+oQBRUMtLJY708+KV2Thztz/hYi2Ufg/zcN8ifrz2bz950SQ5aKFJYTgyqecAnR4aQMeavgCqiSyl1AlXW2mun0tBCUFDJWA692MnP7tuT8XEWyx+I8KN5A9yy+iwaqy/LfuNECshxQQWjwyo21NdEdK0/H3AMqCm25ZNAQSXjGwxHeOire/jdy6GMjksuYa+4aCHfuWttTtonUgiFXJmiE+ggumbfA9ba3SO2+4gujbTRGLOT6KoTOZl7lU8KKknHweeP8tDXfpPxcRbLY6VhTpzl5ZG/v0rl6zItFDKo4ovN+iCxnHQb0eAKWGt3x3tWgM9a+zfZamAhKagkXYPhCD/+wk7ePJRZsYXF8mBZPx1lVuXrMi0UMqh2WmtXxcKoKvZZw/DgChBdNX2LtfaL2WpgISmoJFM7H+5g289ezegYi2Wbe4Cn5kTYdNPFfHjt0hy1TiT3CroorbX2xykeX86p4LqaU8EVIhpcbUR7XAez1eB8UlDJZMQLLSzR1dTTYbE86Q6zfc4Q2z/1J7q3lRQtRxZTDDtZNLjWAdcwPLjarLXXZe2J8kRBJZPV1z3AN/7hqYyOsVh2uAf41ZwILXXvYvXyBTlqnUjuOD6oRp08uqhtFbDAWvvJnD1RjiioZCqOHDxB6+d3ZnSMxfI/JWGenTuk29xLUSq6oCp2CiqZqsFwhJ9+eReHD5xM+xiL5U2GuH9emE+9X0suSXFRUOWZgkqype3be9n/7JG094/Ptfrh7H5uv75CSy5J0chFUJVM8ITLY7egzwljzJ/k6twiTlJ1x0Usu3Rh2vsbDCUY/qy7lB8+1MH/efD5HLZOxNnGDSpr7QGg0RizLNtPHFtmSWTGuOFvLmXldWdndIzB8KGeUp588nf8wwNFP4deZFLSLU//OvDbbMyPilUGNgBfd/qyShr6k1z41Q9e5oUnf5/RMRbLI2VhZp1/Gi0fe0+OWiYydXkf+ouz1v41cMAYs9MYs2kyPSxjzC3GmMeADUSXWHJ0SInkynv/7AJWX5/ZpF6D4bo+Ly8cOM7tzc/kqGUizpRxMYUx5hagDlgJ7CR6P6qO2Ob4UksLiM6hWg1UAMuBZqA5NpxYFNSjklx6ast+nv/F79LeP34Txh/M7uf0c8r52T1X5ahlIpPnqKq/2HJK64jexbeCU+EUFwR2ATuLdYFaBZXkWkf7ER5p3pvRMRbLA7P6sYu9PPXJdTlqmcjkOCqoZgIFleTDoZc6+dm/ZXZvK4sl4A1T9ra5/Ohjl+eoZSKZK9g1KhHJnbMvXMgH/vbSjI4xGNaFvQzsO8HNX3kyRy0TcYaMelSxIopKIDQTiiHUo5J8mmzP6il3mPDb5vDgx6/IUctE0lfQHpUx5l6iRRMtQJsxZr8x5pxsNURkpjv7woVcV3dRRscYDFcMennLiz3c9rVf56hlIoWVVlAZY64GGoEDwOPAceBcoN0Yk9mYhYiMqWLFYi6rynxS8NuH3NiXTnDF59ty1DKRwkl3wu9jRG/V8YWkxyqJTtz1WWtX566JhaOhPymUyVYDPl4aZui82fz041fmqGUi4yvk0N/85JCKNaLdWltFdCLwzdlqkIhAReXiSRVYXN3v5UTHSW5reoa+gUiOWieSX+kG1XjJuB64LQttEZEkk60GvKW3lO6XQlz8fx9hz6FQbhonkkfpBlXXWBustceBY9lpjogkm2xYXdfnpbwf/vSrv+Z4TzhHrRPJj3SDaqKLNPOn2hARSW0yYQWwvruM93S7uPQzbew4MObvmiKOl60Jv+pRieRQpmFlYh/vHPCw5mQJNU3PKKykaKUbVKsmKENXWZxIjp194UJuuPvijI4xGK4c9HJlt4tbVWAhRSrtqj+ic6YixphHjDH3GmMuy2G7RCSFZRcv4ooPnZvRMQbDmgEPy/oNG1ozW/lCxAkyGfqLT/S9hujk313GmE5jzKNM0OMyxmyaWjMzZ4ypNMZsiH22GGN8+W6DSC5cctVSrqvNfAWLW3pL2bnzML946UiOWiaSG+kGVbu19hpr7QKit/SoA35CtGy9iui9qcbrcVVms9ETiYXSKmtto7W2EdhCNGhFpoWKysW86+ZlGR1jMHyou5S6b+3U9SopKumuTPGJkRN+k7YtJ3pfqmuAqzl1T6r4idsBv7V24ZRbmyZjzDqgyVpbEfvaR7TgY761NpTBebQyhTjaL763jxf/5/W097ex2y9+f04/v9x0LWUeV+4aJzNSUdyPaozgstbavP6LMMZUWmvb4/8P7Mr0G6egkmLQ9u297H82s+G8CJbNp/Xxmy/ckKNWyUxVFEE16gmivZstk+lRGWNagE3xwBmxrRJYRfROwj6itx4JjHGeBgBrbX2Gz6+gkqLwo3/eRueh7rT3j9/W/tHyMN/77NXMK/fmqmkywxTljRNj4bEr3f2NMX5jTFMsXNYRvcX9qH2AjdbaZmttwFrbCtTFwmvkvj6gMtOQEikm1RtWZbR/fJ7VtT1eVn+6je8+fSBHLROZunzd4XdUj2gs1tqgtbYuFixjXfGtB5pGPLaJ6GruIzUANek+v0gxcntcVH8yGlaZ9v8/esLLD1tf5quP7ct+w0SyIOdDf1NhjOkA6kYO6RljjgErrbXBpMd8wLHk7qYxZgPQbK0NGWN8mRRSxI7X0J8UlZOhfr7zycxuoBgvsAgDg1ecTv3tusWcTF5RDv1lW2zYz5ccUgDxEIoP/xljqoHWpHBal8dmihTEHF8pV3z4vIyOMRhKMJRh8Dz1JgePnMhR60Qmp+iCilPl72NZEAuzFqDDGGNjPaNUw4JAtOeU6jOLbRbJm0veezarb1g6qWO9wN2ff1rzrMRRijGoJhS7zmVGfFYUul0i+bLmxnM545w5k1qE80963dz+H8/wpcde1tqA4gjTMqgylSLUTDbHV0UK4aZ7V457x9NUDIZSDPecKGPf1oO887MB3XxRCq4YgyoEieKJVDRmIUK0EvCmezNfvSxeun7JgJuPHnFz91eeoWXnIfWupGCKLqhiRRQhRsyvigdXqsnBIjPVmef6uOkfVgCnJvmmy2DwYrj5uJd/evAF1nxOvSspjKILqpgAoxe6XRV7XESSnHnefOruey+mxGQcVhAtsPD3GE70DvLn33hWPSvJu2INqnpg44jH6mKPi8gIbq+Lu754BSbjq1ZRa/tcXBh20dMbofnJ4MQHiGSR4yb8xobwNhItQ68luqpFAGhLnvgbW0Owkuhaf36ityLJao9KE35luvnF9/ex96nXM4qreC9sCBgAfnxaP098TiuvS2pFuShtMVNQyXQzOBCh6Z5fZb7OUpJ+LH+4agFf+NCK7DVMpg2tTCEiU+L2uLju7osnda0qzgu8/vQb/L+te3W9SvJCQSUyw1S8YxFnXLxw0mFlMLwr7KbtiVe5+NOPahULyTkFlcgMdHPtxYkhmskwGG7tLeWCbkNN0zMKK8kpBZXIDOT2uPjA3142pXMYDNf1eZk3AB9ufobjPeHsNE5kBAWVyAx19tsWcNXt5wOZTwZOtr67jDU9Llb9syYES26o6m8cqvqTmeDksT6+s/HpKZ3DYnm8dIDflEW4ZfVb+dT1F+r29jOUytPzTEElM8WhfV387F93T/r4eI8sAmyZ3c/rHssXqy+hetXZ2WmgFA0FVZ4pqGQmafvWC7y87Y1Jrl1xisXyg1hY7fmnqmE9q1BPmH95bD8dR09SsWgO915zvnpe04yCKs8UVDKT7HjoANu3HsjKuSJYvjK3j/m+UrZ+/HLOmDuL1p2HuLf1+VH7quc1vSio8kxBJTPJ/u2Hefw7LzEUmfrr3WIZAp4uHWBHWYSPvnsp33zmtTH3H9nzkuKllSlEJGf8Kxbh9mbnLcFgcGF4T7+Hjx0v4+GnDo27/xcf25+V55XpSUElIkB8btWKrIUVRAOrDMOtJ0txjdNRCx49mbXnlOlHQSUiCYuXzeWuL17BZeuye83IC6zr9oy5/ZyFs7P6fDK9uAvdABFxFrfXxeXV53Ha6aU89aPfZuWcBsPFgy56e4bodMFcawiVWPZ7IkQMhCMR+gYiunWIpKRiinGomEJmuoPPH+Whr/0ma+dLXgFjABgy0DK7n6Mey+wyN/ffuZZLz/Zl7fkk/1RMISJ5teySRdxw98VTuX3VMCbpw4uhzBpqukthCE70DvKRb27TrUNkFAWViIxr2cWL8JSWTGk9wPF4Ldx80ot3CPrDEX7+/Osp9xsMR9i//TA7HjrA/u2HGVSgzRga+huHhv5Eon7+lT0cfKFzyqtWpJIcgP89K8xvZ8MDde8aNgR45OAJtt63m6GIZaA/gqfURYnLcOM9l7F42dwctEomSxN+80xBJRL10jOv88R39uX8eSyWEJb+EkvpPC9LL17IB64/l9bPbKe/Z3D0AS7D0PmncfqZs7nxhnOZUz52ZaHkh4IqzxRUIlGDAxH+8++fJDKY238LqYYXI4DbVQIpVsyI7z8AWANr7ngb71l7Vk7bKONTMYWIFITb4+KCd70lR1epTjEpPtwYbGRo3P29GEqtYfu393GyZyDHrZR8U1CJSFrOOs+Hy+EzLw2w9aHszP0S51BQiUha/CsW4fE6O6k8Ft483F3oZkiWKahEJC1uj4sb77kM4y1hEJv4yAczot5wvOedt7As182RPFNQiUjaFi+by19seje/8kU4iOYxSX6o6m8cqvoTSa092MX37tuFv69kVG8nHyw25fMOYYlgedMDpbPdlBhDSbmbXmMJl5ZQ5inB80YYY+CtFy3g5g9ekLKkvW8gwqN7D/NqZw/nLCzn2ouWaB3CNKk8Pc8UVCKjxSffDoQjRAZtAWJq7KDKdChywMDqESXtew6F+Mg3txGJWHrCEcq9Llwuo3UI06TydBEpqMFwhK337aa/Z5ChAoUUjL5mlfx4Jh/eESXtfQMRPvLNbZzoHaQ7HMEC3eGI1iEsMAWViKQtuPtoVm5V7yQue6qk/dG9h4mM8feLRCyP7j2cz6ZJjIJKRNJ2/GgvA/3Tq1fh4lRJ+6udPfSEU//9esIRXuvsyWPLJE5BJSJpm7doFp7S6VVUEAFOXxK9w/A5C8sp96b++5V7XSxdWJ7HlkmcgkpE0uZfsYgSV6GuTOVGxMCNN5wLwLUXLcE1xt/P5TJce9GSfDZNYlT1Nw5V/YmMNvKWGyXeEvrDEZy9ZkVqNum/AEOxr1L1qYZinxEgvo67CwgDA24ome2id8gy549DlMX2LZ9fyllvmcORvjCdr3djhqBkUSl3/s0KTptXmiiBXzqvjHN6DXv2vcm+P/byx9M9XPX2xdx46ZnRApaHO3jzcDe+RbPwvrWcN145zmmD4DrNw7EFHs45Y3YiRLNZVj+ZMn2Vp+eZgkoktcFwhODuo5x4s5e5p8/izHcs5OsPvID32S4N06QpMCfCK+VDzO0Zorq7lBILHqIrwQ8ZaJndj9ddwp8e9yS2DQLupD/j+/7cN8gb3tj7lCUrZfWTLdNXUOWZgkokPSe7wzTf+xSl2XtvmvYslq+e1sdfnSyjLMX3rS/W0ytLYxJAn7F8bW4fkRS7zp3lZvun1mXUs+obiLDmcwFO9I6+B9hE59M8KhFxpK0Pd2D0+1zGbujxUjLG980FaQ+nGgvnD6QOjsmU1TutTL8Yh5VFxGHePNyN7q2bOZ81Y37fXJD2hGoPMG8o9d6TKat3Wpm+elQiMmWnL5mNbleYuZCxY37fIrHPdAwAx8fomk2mrN5pZfoKKhGZshtvOBddnsrcQ+VhxugIDasunIg1sN+TOtYmU1bvtDJ9BZWITNmccg9r7ngb/cYOu1eVPsb+CMyJUDLLRevsfvqMpR/LENE/+4ylZU4/P/UN0GcsYRPdFo4dGx6x7899g5SVuZhd6mK214UBZntdzJ3l5v4712Zcol7mcXH/nWuZO8udlfNNlar+xqGqP5HMnOwOs/XhDl57pZP+Q33MtuAtdKMyEJ8rleri/UTzqMxsF32p5lGdeRpHevvHnEf1WmcPZyfmUXXy8h97OLHIw1UXTjSPyuA6zU1ogYelI+ZRvdbZw9IszqPK5HwqT88zBZWISGZUni4iIjOOytPTEP8NQURE8k89KhERcTT1qMaRzTHWmSTp2p6+fzOQfv4zW/znn03qUYmIiKMpqERExNEUVCIi4mgKKhERcTQFlYiIOJpWphAREUdTj0pERBxN86gkr4wxlcBma+3KQrdF8s8Ysy72vz5gNbDFWtteuBZJPhljGoAO4AHgVqDLWts60XHqUUneJL1JVRa0IVJILcDO2JvTDmBzgdsj+dcEHAB86YQUqEcleWStDYDWTpzhVlprQ7H/XwB0FbAtkn87JrNiiXpUIpI31tpg0pc1QEOh2iKFE7sEkDb1qCQjxpgWYFOq6wqxF98qIEj0GkQo3ouS6WOqrwFjjB+oBlr0+ig+U/z5+2OXAHbGrleldY1SQSUTir2x1AMhYB3RMeZU+2y01tYkPdZijOnSxfLil83XgLU2aIxpBhqMMdXpXqeQwsnWz99a25i0rQloAyomfH7No5JMGGM6gLoUvyU3MeI35NhvVw3W2qoR+1qtrF28svEaiG1bR/SNan7SdStxuKn8/I0xvuSfdbrvBbpGJdlyK9HufrIg0d++ZGYY9zVgjFlnjNmVtG1n7M8FeWib5N6EP3/g8cmcWEElUxbr8vtGXCgn/ptTphdOpfik+RroArYkbV4FBEceI8UnzZ//TmBT0jHVgMrTJW98E2xfAInfqCpj/98AtOli+rThm2D7AmttwBjjN8bUxh5bCYwaEpSi5Jtg+wJrbbsxJmiM2UD0WldF8vWs8SioJG9ioRQAGifaV6YnFU7MbLGiioyLqzT0JyIijqagkmwIQbSiZ4ztWn1g+guBXgMzWAhy9/NXUMmUxS6ghhhRvRV/0Woe1fSn18DMluufv4JKsiXA6MVmV8Uel5lBr4GZLWc/fwWVZEs9sHHEY3Wxx2Vm0GtgZsvZz18rU8iEYt33jURLUGuJVu0EGFFenlR+HgT8QLvKz6cHvQZmtkL//BVUIiLiaBr6ExERR1NQiYiIoymoRETE0RRUIiLiaAoqERFxNAWViIg4moJKREQcTUElUiSMMdXGmF3GmGPGmGOFbo9IvmjCr0iRiYVU0Fq7stBtEckH9ahEikhsKRsfWuhVZhAFlUhxWRf7c0tBWyGSRwoqkeJSBbq/k8wsukYlUkRi16d2WmurCt0WkXxxF7oBIpIeY4yf6PWptqTHfERvuwBQYa2ty3/LRHJLQ38ixSN+fSoAp+4RZK1tJHp/oFuNMdUFaptIziioRIpH4vpUUkjF755aCXQRDSyRaUXXqESKRPz6FFDD8JASmdbUoxIpAknXpxagkJIZRkElUhyS50/5Yksp1Y53gMh0oao/keJQBYRihRMYYyqBXcYYrLXNhW2aSG6pRyVSHNaRtGxSbMJviFiBBYB6WDJdKahEHC7We/KRNH8qxgcEk76uyFOTRPJKQSXifMPmTyUJAZ2Q6E015bFNInmja1QizrcQCFhrgyMerwHqjTELgR0ptotMC5pHJSIijqahPxERcTQFlYiIOJqCSkREHE1BJSIijqagEhERR1NQiYiIoymoRETE0RRUIiLiaAoqERFxNAWViIg42v8Hg05C809atQMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = plt.cm.tab20.colors\n",
    "ig = plt.figure(figsize=(6, 3))\n",
    "ax = plt.axes()\n",
    "ax.plot(range(len(degree_freq)), degree_freq, marker='o',\n",
    "        linewidth=0, color=cmap[0], markersize=7, label=r'Original', alpha=1)\n",
    "ax.plot(range(len(degree_freq_filtered)), degree_freq_filtered, marker='o',\n",
    "        linewidth=0, color=cmap[8], markersize=7, label=r'Filtered', alpha=1)\n",
    "ax.set_xlabel(r'$k$', fontsize=axisLabelSize)\n",
    "ax.set_xscale('log')\n",
    "ax.set_ylabel(r'$P(k)$', fontsize=axisLabelSize)\n",
    "ax.set_yscale('log')\n",
    "ax.set_ylim(0.4,1e7)\n",
    "ax.set_xlim(0.6,1e5)\n",
    "#ax.set_xticks([10**i for i in range(6)], [10**i for i in range(6)])\n",
    "ax.tick_params(which='major', labelsize=ticksLabelSize, length=7, width=ticksWidth)\n",
    "ax.tick_params(which='minor', length=4, width=ticksWidth)\n",
    "ax.legend(loc='upper right', ncol=1, fontsize=legendSize, frameon=False)\n",
    "plt.savefig(f'{path_output}degree_distribs_OriginalDataset_vs_FilteredDataset.pdf', format='pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3683faf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#### For log-binned distribution\n",
    "import numpy as np\n",
    "def drop_zeros(a_list):\n",
    "    return [i for i in a_list if i>0]\n",
    "\n",
    "def log_binning(counter_dict,bin_count):\n",
    "\n",
    "    max_x = np.log10(max(counter_dict.keys()))\n",
    "    max_y = np.log10(max(counter_dict.values()))\n",
    "    max_base = max([max_x,max_y])\n",
    "\n",
    "    min_x = np.log10(min(drop_zeros(counter_dict.keys())))\n",
    "\n",
    "    bins = np.logspace(min_x,max_base,num=bin_count)\n",
    "\n",
    "    bin_means_y = (np.histogram(list(counter_dict.keys()),bins,weights=list(counter_dict.values()))[0]/np.histogram(list(counter_dict.keys()),bins)[0])\n",
    "    bin_means_x = (np.histogram(list(counter_dict.keys()),bins,weights=list(counter_dict.keys()))[0]/np.histogram(list(counter_dict.keys()),bins)[0])\n",
    "\n",
    "    return bin_means_x,bin_means_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "e9518531",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_6499/3539005830.py:16: RuntimeWarning: invalid value encountered in true_divide\n",
      "  bin_means_y = (np.histogram(list(counter_dict.keys()),bins,weights=list(counter_dict.values()))[0]/np.histogram(list(counter_dict.keys()),bins)[0])\n",
      "/tmp/ipykernel_6499/3539005830.py:17: RuntimeWarning: invalid value encountered in true_divide\n",
      "  bin_means_x = (np.histogram(list(counter_dict.keys()),bins,weights=list(counter_dict.keys()))[0]/np.histogram(list(counter_dict.keys()),bins)[0])\n"
     ]
    }
   ],
   "source": [
    "degree_freq_dict = {d:v for d,v in enumerate(degree_freq)}\n",
    "x_original,y_original = log_binning(degree_freq_dict,100)\n",
    "degree_freq_filtered_dict = {d:v for d,v in enumerate(degree_freq_filtered)}\n",
    "x_filtered,y_filtered = log_binning(degree_freq_filtered_dict,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "f0f636d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = plt.cm.tab20.colors\n",
    "ig = plt.figure(figsize=(6, 3))\n",
    "ax = plt.axes()\n",
    "ax.plot(x_original, y_original, marker='o',mfc='none', markeredgewidth=1.5,\n",
    "        linewidth=0, color=cmap[0], markersize=7, label=r'$\\mathcal{P}_{original}$', alpha=1)\n",
    "ax.plot(x_filtered, y_filtered, marker='s', mfc='none', markeredgewidth=1.5,\n",
    "        linewidth=0, color=cmap[8], markersize=7, label=r'$\\mathcal{P}_{C1}$', alpha=1)\n",
    "ax.set_xlabel(r'$k$', fontsize=axisLabelSize)\n",
    "ax.set_xscale('log')\n",
    "ax.set_ylabel(r'$P(k)$', fontsize=axisLabelSize)\n",
    "ax.set_yscale('log')\n",
    "#ax.vlines(10**2, 10**-4, 10**7, colors='red', linestyles='--')\n",
    "#ax.set_ylim(0.4,1e7)\n",
    "#ax.set_xlim(0.6,1e5)\n",
    "#ax.set_xticks([10**i for i in range(6)], [10**i for i in range(6)])\n",
    "ax.tick_params(which='major', labelsize=ticksLabelSize, length=7, width=ticksWidth)\n",
    "ax.tick_params(which='minor', length=4, width=ticksWidth)\n",
    "ax.legend(loc='upper right', ncol=1, fontsize=legendSize, frameon=False)\n",
    "plt.savefig(f'{path_output}degree_distribs_LogBinned_OriginalDataset_vs_FilteredDataset.pdf', format='pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "163180cc",
   "metadata": {},
   "source": [
    "# 4 - Build an egocentric collaboration network based on all publications (not only in neurosciences) of the authors\n",
    "- Build a dataset of authors authors of the papers included in the set of neuroscience papers $\\mathcal{P}$ (query level 0), based on all their publications, included those that are not in $\\mathcal{P}$ (query level 2, extracted as well).\n",
    "- Build a collaboration network centered on these authors."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32a9dabe",
   "metadata": {},
   "source": [
    "## 4.1 - Build the author database\n",
    "Since many files are used for the process, we recall here the basic steps for building such a database:\n",
    "1) Get the authors from the neuroscience egocentric database (QL0), called `all_papers_QL0` in the following\n",
    "\n",
    "2) Get their informations (name and affilitations) from a file imported under the variable `allAuthors_Ql0Ql1`\n",
    "\n",
    "3) In order to compute their AI score overall their publications, add to the database QL0 the paper published by these authors but not inside QL0, saved in the file loaded under the variable `allpapers_Ql2` in the following. Then compute the AI score."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fa68471",
   "metadata": {},
   "source": [
    "### 4.1.1 - Step 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2292f0bb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# only papers in qeryLevel=0, published since 1970 and that have at least 10 references and 10 impacts\n",
    "papersAI = pd.read_csv(f\"{path_output}papersNeuroAI_minRef10_minImp10_from1970.csv\", sep=';')\n",
    "papersNonAI = pd.read_csv(f\"{path_output}papersNeuro_minRef10_minImp10_from1970.csv\", sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "53e247a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "allpapers_QL0 = pd.concat([papersAI,papersNonAI]).reset_index(drop=True)\n",
    "allpapers_QL0['authors'] = [list(set(eval(i))) if i!='-1' else -1 for i in allpapers_QL0['authors']] # to avoid the double apparitions\n",
    "allpapers_QL0['journal_title'] = [list(set(eval(i))) if i!='-1' else -1 for i in allpapers_QL0['journal_title']] # idem\n",
    "allpapers_QL0['wos_domains'] = [list(set(eval(i))) if i!='-1' else -1 for i in allpapers_QL0['wos_domains']] # idem\n",
    "allpapers_QL0['wos_domains_agg'] = allpapers_QL0['wos_domains'].apply(lambda x: list(set([i.split(', ')[0] for i in x])) if x!=-1 else -1)\n",
    "allpapers_QL0['keywordsIA'] = [list(set(eval(i))) if i!=-1 else -1 for i in allpapers_QL0['keywordsIA']] # idem\n",
    "allpapers_QL0['classIA'] = [list(set(eval(i))) if i!=-1 else -1 for i in allpapers_QL0['classIA']] # idem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "693259e7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>queryLevel</th>\n",
       "      <th>publicationYear</th>\n",
       "      <th>idJournal</th>\n",
       "      <th>authors</th>\n",
       "      <th>journal_title</th>\n",
       "      <th>wos_domains</th>\n",
       "      <th>isIA</th>\n",
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       "      <th>classIA</th>\n",
       "      <th>wos_domains_agg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2617245985</td>\n",
       "      <td>0</td>\n",
       "      <td>2017</td>\n",
       "      <td>45693802</td>\n",
       "      <td>3024761849</td>\n",
       "      <td>[NEUROCOMPUTING]</td>\n",
       "      <td>[Computer Science, Artificial Intelligence]</td>\n",
       "      <td>1</td>\n",
       "      <td>[autoencoder, dimensionality reduction]</td>\n",
       "      <td>[deep learning, cluster analysis]</td>\n",
       "      <td>[Computer Science]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2617245985</td>\n",
       "      <td>0</td>\n",
       "      <td>2017</td>\n",
       "      <td>45693802</td>\n",
       "      <td>2619442247</td>\n",
       "      <td>[NEUROCOMPUTING]</td>\n",
       "      <td>[Computer Science, Artificial Intelligence]</td>\n",
       "      <td>1</td>\n",
       "      <td>[autoencoder, dimensionality reduction]</td>\n",
       "      <td>[deep learning, cluster analysis]</td>\n",
       "      <td>[Computer Science]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021655074</td>\n",
       "      <td>0</td>\n",
       "      <td>2006</td>\n",
       "      <td>140962798</td>\n",
       "      <td>2145260682</td>\n",
       "      <td>[NEURAL PROCESSING LETTERS]</td>\n",
       "      <td>[Computer Science, Artificial Intelligence]</td>\n",
       "      <td>1</td>\n",
       "      <td>[backpropagation, perceptrons]</td>\n",
       "      <td>[neural networks]</td>\n",
       "      <td>[Computer Science]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021655074</td>\n",
       "      <td>0</td>\n",
       "      <td>2006</td>\n",
       "      <td>140962798</td>\n",
       "      <td>2835622326</td>\n",
       "      <td>[NEURAL PROCESSING LETTERS]</td>\n",
       "      <td>[Computer Science, Artificial Intelligence]</td>\n",
       "      <td>1</td>\n",
       "      <td>[backpropagation, perceptrons]</td>\n",
       "      <td>[neural networks]</td>\n",
       "      <td>[Computer Science]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1790968754</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>207023548</td>\n",
       "      <td>2995834608</td>\n",
       "      <td>[NEURAL COMPUTATION]</td>\n",
       "      <td>[Neurosciences, Computer Science, Artificial I...</td>\n",
       "      <td>1</td>\n",
       "      <td>[principal component analysis]</td>\n",
       "      <td>[cluster analysis]</td>\n",
       "      <td>[Neurosciences, Computer Science]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4026968</th>\n",
       "      <td>2046820344</td>\n",
       "      <td>0</td>\n",
       "      <td>2012</td>\n",
       "      <td>116196263</td>\n",
       "      <td>2579365867</td>\n",
       "      <td>[CORTEX]</td>\n",
       "      <td>[Behavioral Sciences, Neurosciences, Psycholog...</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>[Psychology, Behavioral Sciences, Neurosciences]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4026969</th>\n",
       "      <td>2046820344</td>\n",
       "      <td>0</td>\n",
       "      <td>2012</td>\n",
       "      <td>116196263</td>\n",
       "      <td>2016899951</td>\n",
       "      <td>[CORTEX]</td>\n",
       "      <td>[Behavioral Sciences, Neurosciences, Psycholog...</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>[Psychology, Behavioral Sciences, Neurosciences]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4026970</th>\n",
       "      <td>2046820344</td>\n",
       "      <td>0</td>\n",
       "      <td>2012</td>\n",
       "      <td>116196263</td>\n",
       "      <td>157945237</td>\n",
       "      <td>[CORTEX]</td>\n",
       "      <td>[Behavioral Sciences, Neurosciences, Psycholog...</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>[Psychology, Behavioral Sciences, Neurosciences]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4026971</th>\n",
       "      <td>2046820344</td>\n",
       "      <td>0</td>\n",
       "      <td>2012</td>\n",
       "      <td>116196263</td>\n",
       "      <td>2496737467</td>\n",
       "      <td>[CORTEX]</td>\n",
       "      <td>[Behavioral Sciences, Neurosciences, Psycholog...</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>[Psychology, Behavioral Sciences, Neurosciences]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4026972</th>\n",
       "      <td>2046820344</td>\n",
       "      <td>0</td>\n",
       "      <td>2012</td>\n",
       "      <td>116196263</td>\n",
       "      <td>2600298751</td>\n",
       "      <td>[CORTEX]</td>\n",
       "      <td>[Behavioral Sciences, Neurosciences, Psycholog...</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>[Psychology, Behavioral Sciences, Neurosciences]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4026973 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id  queryLevel  publicationYear  idJournal     authors  \\\n",
       "0        2617245985           0             2017   45693802  3024761849   \n",
       "1        2617245985           0             2017   45693802  2619442247   \n",
       "2        2021655074           0             2006  140962798  2145260682   \n",
       "3        2021655074           0             2006  140962798  2835622326   \n",
       "4        1790968754           0             2015  207023548  2995834608   \n",
       "...             ...         ...              ...        ...         ...   \n",
       "4026968  2046820344           0             2012  116196263  2579365867   \n",
       "4026969  2046820344           0             2012  116196263  2016899951   \n",
       "4026970  2046820344           0             2012  116196263   157945237   \n",
       "4026971  2046820344           0             2012  116196263  2496737467   \n",
       "4026972  2046820344           0             2012  116196263  2600298751   \n",
       "\n",
       "                       journal_title  \\\n",
       "0                   [NEUROCOMPUTING]   \n",
       "1                   [NEUROCOMPUTING]   \n",
       "2        [NEURAL PROCESSING LETTERS]   \n",
       "3        [NEURAL PROCESSING LETTERS]   \n",
       "4               [NEURAL COMPUTATION]   \n",
       "...                              ...   \n",
       "4026968                     [CORTEX]   \n",
       "4026969                     [CORTEX]   \n",
       "4026970                     [CORTEX]   \n",
       "4026971                     [CORTEX]   \n",
       "4026972                     [CORTEX]   \n",
       "\n",
       "                                               wos_domains  isIA  \\\n",
       "0              [Computer Science, Artificial Intelligence]     1   \n",
       "1              [Computer Science, Artificial Intelligence]     1   \n",
       "2              [Computer Science, Artificial Intelligence]     1   \n",
       "3              [Computer Science, Artificial Intelligence]     1   \n",
       "4        [Neurosciences, Computer Science, Artificial I...     1   \n",
       "...                                                    ...   ...   \n",
       "4026968  [Behavioral Sciences, Neurosciences, Psycholog...     0   \n",
       "4026969  [Behavioral Sciences, Neurosciences, Psycholog...     0   \n",
       "4026970  [Behavioral Sciences, Neurosciences, Psycholog...     0   \n",
       "4026971  [Behavioral Sciences, Neurosciences, Psycholog...     0   \n",
       "4026972  [Behavioral Sciences, Neurosciences, Psycholog...     0   \n",
       "\n",
       "                                      keywordsIA  \\\n",
       "0        [autoencoder, dimensionality reduction]   \n",
       "1        [autoencoder, dimensionality reduction]   \n",
       "2                 [backpropagation, perceptrons]   \n",
       "3                 [backpropagation, perceptrons]   \n",
       "4                 [principal component analysis]   \n",
       "...                                          ...   \n",
       "4026968                                       -1   \n",
       "4026969                                       -1   \n",
       "4026970                                       -1   \n",
       "4026971                                       -1   \n",
       "4026972                                       -1   \n",
       "\n",
       "                                   classIA  \\\n",
       "0        [deep learning, cluster analysis]   \n",
       "1        [deep learning, cluster analysis]   \n",
       "2                        [neural networks]   \n",
       "3                        [neural networks]   \n",
       "4                       [cluster analysis]   \n",
       "...                                    ...   \n",
       "4026968                                 -1   \n",
       "4026969                                 -1   \n",
       "4026970                                 -1   \n",
       "4026971                                 -1   \n",
       "4026972                                 -1   \n",
       "\n",
       "                                          wos_domains_agg  \n",
       "0                                      [Computer Science]  \n",
       "1                                      [Computer Science]  \n",
       "2                                      [Computer Science]  \n",
       "3                                      [Computer Science]  \n",
       "4                       [Neurosciences, Computer Science]  \n",
       "...                                                   ...  \n",
       "4026968  [Psychology, Behavioral Sciences, Neurosciences]  \n",
       "4026969  [Psychology, Behavioral Sciences, Neurosciences]  \n",
       "4026970  [Psychology, Behavioral Sciences, Neurosciences]  \n",
       "4026971  [Psychology, Behavioral Sciences, Neurosciences]  \n",
       "4026972  [Psychology, Behavioral Sciences, Neurosciences]  \n",
       "\n",
       "[4026973 rows x 11 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# expand the big dataframe with respect to the authors\n",
    "allpapers_QL0_expanded = allpapers_QL0.explode('authors').reset_index(drop=True)\n",
    "allpapers_QL0_expanded"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91cdf1a8",
   "metadata": {},
   "source": [
    "### 4.1.2 - Step 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c4df971f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>authorId</th>\n",
       "      <th>name</th>\n",
       "      <th>affiliation</th>\n",
       "      <th>queryLevel</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1990375167</td>\n",
       "      <td>A.A.A.A Kusumawardhani</td>\n",
       "      <td>NONE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2663910298</td>\n",
       "      <td>A.A.A.A Nasser</td>\n",
       "      <td>NONE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2814684889</td>\n",
       "      <td>A A A Aarts</td>\n",
       "      <td>99464096</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3020917786</td>\n",
       "      <td>A.A.A. Ahmed</td>\n",
       "      <td>NONE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2594650676</td>\n",
       "      <td>A. A. A. Aioub</td>\n",
       "      <td>7597260</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15325785</th>\n",
       "      <td>2257106621</td>\n",
       "      <td>李哲雨</td>\n",
       "      <td>NONE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15325786</th>\n",
       "      <td>2516757462</td>\n",
       "      <td>李桂楨</td>\n",
       "      <td>NONE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15325787</th>\n",
       "      <td>2663157117</td>\n",
       "      <td>李永麟</td>\n",
       "      <td>NONE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15325788</th>\n",
       "      <td>2668056676</td>\n",
       "      <td>李相哲</td>\n",
       "      <td>NONE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15325789</th>\n",
       "      <td>2970833545</td>\n",
       "      <td>塚越 慎</td>\n",
       "      <td>NONE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15325790 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            authorId                    name affiliation  queryLevel\n",
       "0         1990375167  A.A.A.A Kusumawardhani        NONE           1\n",
       "1         2663910298          A.A.A.A Nasser        NONE           1\n",
       "2         2814684889             A A A Aarts    99464096           1\n",
       "3         3020917786            A.A.A. Ahmed        NONE           1\n",
       "4         2594650676          A. A. A. Aioub     7597260           1\n",
       "...              ...                     ...         ...         ...\n",
       "15325785  2257106621                     李哲雨        NONE           1\n",
       "15325786  2516757462                     李桂楨        NONE           1\n",
       "15325787  2663157117                     李永麟        NONE           1\n",
       "15325788  2668056676                     李相哲        NONE           1\n",
       "15325789  2970833545                    塚越 慎        NONE           1\n",
       "\n",
       "[15325790 rows x 4 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read the whole authors' dataframe including the authors of our publications (QL0)\n",
    "# as well as those in references and impact (QL1)\n",
    "allAuthors_Ql0Ql1 = pd.read_csv(f'{path_input}authorId_name_affiliation.csv', sep=',')\n",
    "allAuthors_Ql0Ql1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "61728b2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>queryLevel</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1550607070</td>\n",
       "      <td>A. A. A. M. Danen-van Oorschot</td>\n",
       "      <td>121797337</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2505675405</td>\n",
       "      <td>A. A. A. Manik J. Djelantik</td>\n",
       "      <td>193662353</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2806045187</td>\n",
       "      <td>A.A.A. Pradhan</td>\n",
       "      <td>5023651</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>2974182613</td>\n",
       "      <td>A. A. Aarnisalo</td>\n",
       "      <td>NONE</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>2991992674</td>\n",
       "      <td>A A Abdel-Latif</td>\n",
       "      <td>25041050</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>15262197</th>\n",
       "      <td>2263707641</td>\n",
       "      <td>Е. Viteva</td>\n",
       "      <td>NONE</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15262570</th>\n",
       "      <td>3022633000</td>\n",
       "      <td>Е Е Garanina</td>\n",
       "      <td>21203515</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15265271</th>\n",
       "      <td>2688406403</td>\n",
       "      <td>Мikhail Pashenkov</td>\n",
       "      <td>NONE</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15269510</th>\n",
       "      <td>2495571327</td>\n",
       "      <td>І. L. Popovych</td>\n",
       "      <td>149851306</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15303384</th>\n",
       "      <td>2732206803</td>\n",
       "      <td>聡 宇多</td>\n",
       "      <td>42766147</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1290141 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            authorId                            name affiliation  queryLevel\n",
       "41        1550607070  A. A. A. M. Danen-van Oorschot   121797337           0\n",
       "44        2505675405     A. A. A. Manik J. Djelantik   193662353           0\n",
       "53        2806045187                  A.A.A. Pradhan     5023651           0\n",
       "72        2974182613                 A. A. Aarnisalo        NONE           0\n",
       "134       2991992674                 A A Abdel-Latif    25041050           0\n",
       "...              ...                             ...         ...         ...\n",
       "15262197  2263707641                       Е. Viteva        NONE           0\n",
       "15262570  3022633000                    Е Е Garanina    21203515           0\n",
       "15265271  2688406403               Мikhail Pashenkov        NONE           0\n",
       "15269510  2495571327                  І. L. Popovych   149851306           0\n",
       "15303384  2732206803                            聡 宇多    42766147           0\n",
       "\n",
       "[1290141 rows x 4 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter the big file for all authors in QL0 (data for neuroscience)\n",
    "authorsInPapers = allAuthors_Ql0Ql1[allAuthors_Ql0Ql1['authorId'].isin(set(allpapers_QL0_expanded['authors'])-{-1})]\n",
    "authorsInPapers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4d9ca13",
   "metadata": {},
   "source": [
    "### 4.1.3 - Step 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "630a05c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>authorId</th>\n",
       "      <th>paperId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2951562659</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2535012878</td>\n",
       "      <td>348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2482451074</td>\n",
       "      <td>348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2060325654</td>\n",
       "      <td>527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2746639708</td>\n",
       "      <td>662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53855474</th>\n",
       "      <td>2785152602</td>\n",
       "      <td>3035763607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53855475</th>\n",
       "      <td>2852722787</td>\n",
       "      <td>3035763695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53855476</th>\n",
       "      <td>2650729909</td>\n",
       "      <td>3035763695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53855477</th>\n",
       "      <td>2159918643</td>\n",
       "      <td>3035763702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53855478</th>\n",
       "      <td>2250875688</td>\n",
       "      <td>3035763711</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>52243904 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            authorId     paperId\n",
       "0         2951562659         285\n",
       "1         2535012878         348\n",
       "2         2482451074         348\n",
       "3         2060325654         527\n",
       "4         2746639708         662\n",
       "...              ...         ...\n",
       "53855474  2785152602  3035763607\n",
       "53855475  2852722787  3035763695\n",
       "53855476  2650729909  3035763695\n",
       "53855477  2159918643  3035763702\n",
       "53855478  2250875688  3035763711\n",
       "\n",
       "[52243904 rows x 2 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# open the link file of all authors with all their associated papers (QL0+QL2)\n",
    "# drop duplicates: only unique link author-paper\n",
    "allAuthors_Ql0_papers = pd.read_csv(f'{path_input}author2paper.csv', sep=',')\n",
    "allAuthors_Ql0_papers.drop_duplicates(inplace=True)\n",
    "#allAuthors_Ql0_papers = allAuthors_Ql0_papers.groupby('authorId').aggregate(list)\n",
    "allAuthors_Ql0_papers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "68d88d41",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>authorId</th>\n",
       "      <th>name</th>\n",
       "      <th>affiliation</th>\n",
       "      <th>queryLevel</th>\n",
       "      <th>nb_publi_all</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1550607070</td>\n",
       "      <td>A. A. A. M. Danen-van Oorschot</td>\n",
       "      <td>121797337</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2505675405</td>\n",
       "      <td>A. A. A. Manik J. Djelantik</td>\n",
       "      <td>193662353</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2806045187</td>\n",
       "      <td>A.A.A. Pradhan</td>\n",
       "      <td>5023651</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2974182613</td>\n",
       "      <td>A. A. Aarnisalo</td>\n",
       "      <td>NONE</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2991992674</td>\n",
       "      <td>A A Abdel-Latif</td>\n",
       "      <td>25041050</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290136</th>\n",
       "      <td>2263707641</td>\n",
       "      <td>Е. Viteva</td>\n",
       "      <td>NONE</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290137</th>\n",
       "      <td>3022633000</td>\n",
       "      <td>Е Е Garanina</td>\n",
       "      <td>21203515</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290138</th>\n",
       "      <td>2688406403</td>\n",
       "      <td>Мikhail Pashenkov</td>\n",
       "      <td>NONE</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290139</th>\n",
       "      <td>2495571327</td>\n",
       "      <td>І. L. Popovych</td>\n",
       "      <td>149851306</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290140</th>\n",
       "      <td>2732206803</td>\n",
       "      <td>聡 宇多</td>\n",
       "      <td>42766147</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1290141 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           authorId                            name affiliation  queryLevel  \\\n",
       "0        1550607070  A. A. A. M. Danen-van Oorschot   121797337           0   \n",
       "1        2505675405     A. A. A. Manik J. Djelantik   193662353           0   \n",
       "2        2806045187                  A.A.A. Pradhan     5023651           0   \n",
       "3        2974182613                 A. A. Aarnisalo        NONE           0   \n",
       "4        2991992674                 A A Abdel-Latif    25041050           0   \n",
       "...             ...                             ...         ...         ...   \n",
       "1290136  2263707641                       Е. Viteva        NONE           0   \n",
       "1290137  3022633000                    Е Е Garanina    21203515           0   \n",
       "1290138  2688406403               Мikhail Pashenkov        NONE           0   \n",
       "1290139  2495571327                  І. L. Popovych   149851306           0   \n",
       "1290140  2732206803                            聡 宇多    42766147           0   \n",
       "\n",
       "         nb_publi_all  \n",
       "0                   6  \n",
       "1                  11  \n",
       "2                   1  \n",
       "3                   2  \n",
       "4                   8  \n",
       "...               ...  \n",
       "1290136             1  \n",
       "1290137             1  \n",
       "1290138             1  \n",
       "1290139             1  \n",
       "1290140             1  \n",
       "\n",
       "[1290141 rows x 5 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add the total number of publications of the authors above\n",
    "authorsInPapers = authorsInPapers.merge(pd.DataFrame.from_records(list(Counter(allAuthors_Ql0_papers['authorId']).items()), columns=['authorId', 'nb_publi_all']),\n",
    "                                   on='authorId',\n",
    "                                   how='outer').fillna(0)\n",
    "authorsInPapers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4d24a91f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>publicationYear</th>\n",
       "      <th>title</th>\n",
       "      <th>idJournal</th>\n",
       "      <th>queryLevel</th>\n",
       "      <th>journal_title</th>\n",
       "      <th>wos_domains</th>\n",
       "      <th>isIA</th>\n",
       "      <th>keywordsIA</th>\n",
       "      <th>classIA</th>\n",
       "      <th>wos_domains_agg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1984623903</td>\n",
       "      <td>1981</td>\n",
       "      <td>\"F&amp;#x00FC;nf neue Arten der Gattung Halacarus ...</td>\n",
       "      <td>4078192</td>\n",
       "      <td>2</td>\n",
       "      <td>ZOOLOGICA SCRIPTA</td>\n",
       "      <td>[Zoology, Evolutionary Biology]</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>[Zoology, Evolutionary Biology]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1984624469</td>\n",
       "      <td>2014</td>\n",
       "      <td>\"Ultracompact three-dimensional tubular conduc...</td>\n",
       "      <td>143846845</td>\n",
       "      <td>2</td>\n",
       "      <td>NANO LETTERS</td>\n",
       "      <td>[Physics, Condensed Matter, Nanoscience &amp; Nano...</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>[Materials Science, Physics, Chemistry, Nanosc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1984630132</td>\n",
       "      <td>2000</td>\n",
       "      <td>\"Enhanced dechlorination of Aroclor 1242 in an...</td>\n",
       "      <td>52566953</td>\n",
       "      <td>2</td>\n",
       "      <td>WATER RESEARCH</td>\n",
       "      <td>[Environmental Sciences, Engineering, Environm...</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>[Engineering, Environmental Sciences, Water Re...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1984632460</td>\n",
       "      <td>1996</td>\n",
       "      <td>\"NEST PREDATION AND AVIAN LIFE-HISTORY EVOLUTI...</td>\n",
       "      <td>122176235</td>\n",
       "      <td>2</td>\n",
       "      <td>AMERICAN NATURALIST</td>\n",
       "      <td>[Evolutionary Biology, Ecology]</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>[Evolutionary Biology, Ecology]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984633284</td>\n",
       "      <td>2017</td>\n",
       "      <td>\"On the Matter of Suffering: Derek Parfit and ...</td>\n",
       "      <td>56047584</td>\n",
       "      <td>2</td>\n",
       "      <td>CRIMINAL LAW AND PHILOSOPHY</td>\n",
       "      <td>[Law]</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>[Law]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24330847</th>\n",
       "      <td>2523645934</td>\n",
       "      <td>2013</td>\n",
       "      <td>\"Reproductive outcomes after bariatric surgery\"</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24330848</th>\n",
       "      <td>2523663736</td>\n",
       "      <td>2014</td>\n",
       "      <td>\"Nano-engineered PLLA based biomaterial drives...</td>\n",
       "      <td>68700368</td>\n",
       "      <td>2</td>\n",
       "      <td>JOURNAL OF TISSUE ENGINEERING AND REGENERATIVE...</td>\n",
       "      <td>[Cell &amp; Tissue Engineering, Cell Biology, Biot...</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>[Engineering, Cell &amp; Tissue Engineering, Biote...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24330849</th>\n",
       "      <td>2523695446</td>\n",
       "      <td>2016</td>\n",
       "      <td>\"Executive functioning in autism spectrum diso...</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24330850</th>\n",
       "      <td>2523696321</td>\n",
       "      <td>2006</td>\n",
       "      <td>\"Surr&amp;#x00E9;nalectorme unilat&amp;#x00E9;rale et ...</td>\n",
       "      <td>198822128</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24330851</th>\n",
       "      <td>2523716332</td>\n",
       "      <td>2010</td>\n",
       "      <td>\"20. Task-Centered Practice in Japan\"</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24330852 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id  publicationYear  \\\n",
       "0         1984623903             1981   \n",
       "1         1984624469             2014   \n",
       "2         1984630132             2000   \n",
       "3         1984632460             1996   \n",
       "4         1984633284             2017   \n",
       "...              ...              ...   \n",
       "24330847  2523645934             2013   \n",
       "24330848  2523663736             2014   \n",
       "24330849  2523695446             2016   \n",
       "24330850  2523696321             2006   \n",
       "24330851  2523716332             2010   \n",
       "\n",
       "                                                      title  idJournal  \\\n",
       "0         \"F&#x00FC;nf neue Arten der Gattung Halacarus ...    4078192   \n",
       "1         \"Ultracompact three-dimensional tubular conduc...  143846845   \n",
       "2         \"Enhanced dechlorination of Aroclor 1242 in an...   52566953   \n",
       "3         \"NEST PREDATION AND AVIAN LIFE-HISTORY EVOLUTI...  122176235   \n",
       "4         \"On the Matter of Suffering: Derek Parfit and ...   56047584   \n",
       "...                                                     ...        ...   \n",
       "24330847    \"Reproductive outcomes after bariatric surgery\"         -1   \n",
       "24330848  \"Nano-engineered PLLA based biomaterial drives...   68700368   \n",
       "24330849  \"Executive functioning in autism spectrum diso...         -1   \n",
       "24330850  \"Surr&#x00E9;nalectorme unilat&#x00E9;rale et ...  198822128   \n",
       "24330851              \"20. Task-Centered Practice in Japan\"         -1   \n",
       "\n",
       "          queryLevel                                      journal_title  \\\n",
       "0                  2                                  ZOOLOGICA SCRIPTA   \n",
       "1                  2                                       NANO LETTERS   \n",
       "2                  2                                     WATER RESEARCH   \n",
       "3                  2                                AMERICAN NATURALIST   \n",
       "4                  2                        CRIMINAL LAW AND PHILOSOPHY   \n",
       "...              ...                                                ...   \n",
       "24330847           2                                                 -1   \n",
       "24330848           2  JOURNAL OF TISSUE ENGINEERING AND REGENERATIVE...   \n",
       "24330849           2                                                 -1   \n",
       "24330850           2                                                 -1   \n",
       "24330851           2                                                 -1   \n",
       "\n",
       "                                                wos_domains  isIA keywordsIA  \\\n",
       "0                           [Zoology, Evolutionary Biology]     0         -1   \n",
       "1         [Physics, Condensed Matter, Nanoscience & Nano...     0         -1   \n",
       "2         [Environmental Sciences, Engineering, Environm...     0         -1   \n",
       "3                           [Evolutionary Biology, Ecology]     0         -1   \n",
       "4                                                     [Law]     0         -1   \n",
       "...                                                     ...   ...        ...   \n",
       "24330847                                                 -1     0         -1   \n",
       "24330848  [Cell & Tissue Engineering, Cell Biology, Biot...     0         -1   \n",
       "24330849                                                 -1     0         -1   \n",
       "24330850                                                 -1     0         -1   \n",
       "24330851                                                 -1     0         -1   \n",
       "\n",
       "         classIA                                    wos_domains_agg  \n",
       "0             -1                    [Zoology, Evolutionary Biology]  \n",
       "1             -1  [Materials Science, Physics, Chemistry, Nanosc...  \n",
       "2             -1  [Engineering, Environmental Sciences, Water Re...  \n",
       "3             -1                    [Evolutionary Biology, Ecology]  \n",
       "4             -1                                              [Law]  \n",
       "...          ...                                                ...  \n",
       "24330847      -1                                                 -1  \n",
       "24330848      -1  [Engineering, Cell & Tissue Engineering, Biote...  \n",
       "24330849      -1                                                 -1  \n",
       "24330850      -1                                                 -1  \n",
       "24330851      -1                                                 -1  \n",
       "\n",
       "[24330852 rows x 11 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# open the main file of publication containing all additionnal publication of the authors\n",
    "allpapers_QL2 = pd.read_csv(f'{path_input}ExtraPapers_from_authors.csv', sep=';').fillna(-1)\n",
    "allpapers_QL2['wos_domains'] = [list(set(eval(i))) if i!=-1 else -1 for i in allpapers_QL2['wos_domains']]\n",
    "allpapers_QL2['wos_domains_agg'] = allpapers_QL2['wos_domains'].apply(lambda x: list(set([i.split(', ')[0] for i in x])) if x!=-1 else -1)\n",
    "allpapers_QL2['journal_title'] =  allpapers_QL2['journal_title'].apply(lambda x: list(set(eval(x)))[0] if x!=-1 else -1)\n",
    "allpapers_QL2['keywordsIA'] = [list(set(eval(i))) if i!=-1 else -1 for i in allpapers_QL2['keywordsIA']]\n",
    "allpapers_QL2['classIA'] = [list(set(eval(i))) if i!=-1 else -1 for i in allpapers_QL2['classIA']]\n",
    "allpapers_QL2['queryLevel'] = 2\n",
    "allpapers_QL2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bda74724",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>authorId</th>\n",
       "      <th>id</th>\n",
       "      <th>isIA</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2951562659</td>\n",
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       "      <td>2535012878</td>\n",
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       "      <th>2</th>\n",
       "      <td>2482451074</td>\n",
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       "      <th>3</th>\n",
       "      <td>2060325654</td>\n",
       "      <td>527</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>2746639708</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>4026968</th>\n",
       "      <td>2579365867</td>\n",
       "      <td>2046820344</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4026969</th>\n",
       "      <td>2016899951</td>\n",
       "      <td>2046820344</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4026970</th>\n",
       "      <td>157945237</td>\n",
       "      <td>2046820344</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>4026971</th>\n",
       "      <td>2496737467</td>\n",
       "      <td>2046820344</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4026972</th>\n",
       "      <td>2600298751</td>\n",
       "      <td>2046820344</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>56270877 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           authorId          id  isIA\n",
       "0        2951562659         285   0.0\n",
       "1        2535012878         348   0.0\n",
       "2        2482451074         348   0.0\n",
       "3        2060325654         527   0.0\n",
       "4        2746639708         662   0.0\n",
       "...             ...         ...   ...\n",
       "4026968  2579365867  2046820344   0.0\n",
       "4026969  2016899951  2046820344   0.0\n",
       "4026970   157945237  2046820344   0.0\n",
       "4026971  2496737467  2046820344   0.0\n",
       "4026972  2600298751  2046820344   0.0\n",
       "\n",
       "[56270877 rows x 3 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add the indicator 'isIA' from the QL2 dataset\n",
    "allAuthors_Ql0_papers.rename(columns={'paperId':'id'}, inplace=True)\n",
    "allAuthors_Ql0_papers = pd.merge(allAuthors_Ql0_papers, allpapers_QL2[['id','isIA']], on='id', how='left')\n",
    "\n",
    "# add the indicator 'isIA' from the QL0 dataset by concatenation\n",
    "allAuthors_Ql0_papers = pd.concat([allAuthors_Ql0_papers, \n",
    "                                   allpapers_QL0_expanded[['authors','id','isIA']].rename(columns={'authors':'authorId'})])\n",
    "allAuthors_Ql0_papers          "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e8f5895",
   "metadata": {},
   "source": [
    "Compute the total AI score of the authors, based on the publications that are including abstracts. Be careful to skip `NaN` values due to double publications occurring in the cell above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "20f687f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>authorId</th>\n",
       "      <th>AIscore_all</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18161</td>\n",
       "      <td>0.026316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20361</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39506</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57623</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290137</th>\n",
       "      <td>3035759232</td>\n",
       "      <td>0.024390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290138</th>\n",
       "      <td>3035760453</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290139</th>\n",
       "      <td>3035760604</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290140</th>\n",
       "      <td>3035761758</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290141</th>\n",
       "      <td>3035762904</td>\n",
       "      <td>0.037736</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1290142 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           authorId  AIscore_all\n",
       "0                -1     0.166667\n",
       "1             18161     0.026316\n",
       "2             20361     0.000000\n",
       "3             39506     0.000000\n",
       "4             57623     0.000000\n",
       "...             ...          ...\n",
       "1290137  3035759232     0.024390\n",
       "1290138  3035760453     0.000000\n",
       "1290139  3035760604     0.000000\n",
       "1290140  3035761758     0.000000\n",
       "1290141  3035762904     0.037736\n",
       "\n",
       "[1290142 rows x 2 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_AIscore = allAuthors_Ql0_papers[['authorId','isIA']].groupby('authorId').aggregate(np.nanmean).reset_index(level=0)\n",
    "total_AIscore.rename(columns={'isIA':'AIscore_all'}, inplace=True)\n",
    "total_AIscore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "13131990",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>authorId</th>\n",
       "      <th>name</th>\n",
       "      <th>affiliation</th>\n",
       "      <th>queryLevel</th>\n",
       "      <th>nb_publi_all</th>\n",
       "      <th>AIscore_all</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1550607070</td>\n",
       "      <td>A. A. A. M. Danen-van Oorschot</td>\n",
       "      <td>121797337</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2505675405</td>\n",
       "      <td>A. A. A. Manik J. Djelantik</td>\n",
       "      <td>193662353</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2806045187</td>\n",
       "      <td>A.A.A. Pradhan</td>\n",
       "      <td>5023651</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2974182613</td>\n",
       "      <td>A. A. Aarnisalo</td>\n",
       "      <td>NONE</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2991992674</td>\n",
       "      <td>A A Abdel-Latif</td>\n",
       "      <td>25041050</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290136</th>\n",
       "      <td>2263707641</td>\n",
       "      <td>Е. Viteva</td>\n",
       "      <td>NONE</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290137</th>\n",
       "      <td>3022633000</td>\n",
       "      <td>Е Е Garanina</td>\n",
       "      <td>21203515</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290138</th>\n",
       "      <td>2688406403</td>\n",
       "      <td>Мikhail Pashenkov</td>\n",
       "      <td>NONE</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290139</th>\n",
       "      <td>2495571327</td>\n",
       "      <td>І. L. Popovych</td>\n",
       "      <td>149851306</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290140</th>\n",
       "      <td>2732206803</td>\n",
       "      <td>聡 宇多</td>\n",
       "      <td>42766147</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1290141 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           authorId                            name affiliation  queryLevel  \\\n",
       "0        1550607070  A. A. A. M. Danen-van Oorschot   121797337           0   \n",
       "1        2505675405     A. A. A. Manik J. Djelantik   193662353           0   \n",
       "2        2806045187                  A.A.A. Pradhan     5023651           0   \n",
       "3        2974182613                 A. A. Aarnisalo        NONE           0   \n",
       "4        2991992674                 A A Abdel-Latif    25041050           0   \n",
       "...             ...                             ...         ...         ...   \n",
       "1290136  2263707641                       Е. Viteva        NONE           0   \n",
       "1290137  3022633000                    Е Е Garanina    21203515           0   \n",
       "1290138  2688406403               Мikhail Pashenkov        NONE           0   \n",
       "1290139  2495571327                  І. L. Popovych   149851306           0   \n",
       "1290140  2732206803                            聡 宇多    42766147           0   \n",
       "\n",
       "         nb_publi_all  AIscore_all  \n",
       "0                   6          0.0  \n",
       "1                  11          0.0  \n",
       "2                   1          0.0  \n",
       "3                   2          0.0  \n",
       "4                   8          0.0  \n",
       "...               ...          ...  \n",
       "1290136             1          0.0  \n",
       "1290137             1          0.0  \n",
       "1290138             1          0.0  \n",
       "1290139             1          0.0  \n",
       "1290140             1          0.0  \n",
       "\n",
       "[1290141 rows x 6 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add the total AI score of the authors above\n",
    "authorsInPapers = authorsInPapers.merge(total_AIscore,\n",
    "                                   on='authorId',\n",
    "                                   how='inner')\n",
    "authorsInPapers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14c74f5f",
   "metadata": {},
   "source": [
    "### 4.1.4 - Save file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "77bcf1f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "authorsInPapers.to_csv(f'{path_output}collabNet_nodes.csv', sep=';', index=False)\n",
    "# save another file for Gephi\n",
    "authorsInPapers.rename(columns={'authorId':'id','name':'label'}).to_csv(f'{path_output}/collabNet_nodes_gephi.csv', sep=';', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36869535",
   "metadata": {},
   "source": [
    "## 4.2 - Enrich the previous dataset with additional temporal and disciplinairy trajectories of the authors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "87efec02",
   "metadata": {},
   "outputs": [],
   "source": [
    "authorsInPapers = pd.read_csv(f'{path_output}collabNet_nodes.csv', sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d3e4f064",
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Concatenate the big files of papers\n",
    "papers_years = pd.concat([allpapers_QL0[['id','queryLevel','publicationYear','idJournal','wos_domains_agg']],\n",
    "                          allpapers_QL2[['id','queryLevel','publicationYear','idJournal','wos_domains_agg']]]).rename(columns={'id':'paperId'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1769b74e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "487"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del allpapers_QL0, allpapers_QL2\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6deb5142",
   "metadata": {},
   "outputs": [],
   "source": [
    "allAuthors_Ql0_papers.rename(columns={'id':'paperId'},inplace=True)\n",
    "allAuthors_Ql0_papers = allAuthors_Ql0_papers[allAuthors_Ql0_papers['id'].isin(papers_years['paperId'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "41fcf92b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>authorId</th>\n",
       "      <th>publicationYear</th>\n",
       "      <th>wos_domains_agg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2951562659</td>\n",
       "      <td>2012</td>\n",
       "      <td>[Physics, Astronomy &amp; Astrophysics]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2535012878</td>\n",
       "      <td>2004</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2482451074</td>\n",
       "      <td>2004</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2060325654</td>\n",
       "      <td>2000</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2746639708</td>\n",
       "      <td>2001</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51963399</th>\n",
       "      <td>2768276645</td>\n",
       "      <td>2020</td>\n",
       "      <td>[Gastroenterology &amp; Hepatology]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51963400</th>\n",
       "      <td>2328000574</td>\n",
       "      <td>2020</td>\n",
       "      <td>[Gastroenterology &amp; Hepatology]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51963401</th>\n",
       "      <td>2653064050</td>\n",
       "      <td>2020</td>\n",
       "      <td>[Medicine, Oncology]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51963402</th>\n",
       "      <td>2831405835</td>\n",
       "      <td>2020</td>\n",
       "      <td>[Engineering, Materials Science]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51963403</th>\n",
       "      <td>2133172116</td>\n",
       "      <td>2020</td>\n",
       "      <td>[Engineering, Materials Science]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>51963404 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            authorId  publicationYear                      wos_domains_agg\n",
       "0         2951562659             2012  [Physics, Astronomy & Astrophysics]\n",
       "1         2535012878             2004                                   -1\n",
       "2         2482451074             2004                                   -1\n",
       "3         2060325654             2000                                   -1\n",
       "4         2746639708             2001                                   -1\n",
       "...              ...              ...                                  ...\n",
       "51963399  2768276645             2020      [Gastroenterology & Hepatology]\n",
       "51963400  2328000574             2020      [Gastroenterology & Hepatology]\n",
       "51963401  2653064050             2020                 [Medicine, Oncology]\n",
       "51963402  2831405835             2020     [Engineering, Materials Science]\n",
       "51963403  2133172116             2020     [Engineering, Materials Science]\n",
       "\n",
       "[51963404 rows x 3 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "authors_years_wos = pd.merge(allAuthors_Ql0_papers, papers_years, on='paperId', how='left')[['authorId','publicationYear','wos_domains_agg']]\n",
    "authors_years_wos.fillna(-1, inplace=True)\n",
    "#authors_years_wos['publicationYear'] = authors_years_wos['publicationYear'].astype(int)\n",
    "authors_years_wos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "052e6393",
   "metadata": {},
   "outputs": [],
   "source": [
    "authors_years_wos = authors_years_wos[authors_years_wos['publicationYear']<2020]\n",
    "authors_years_wos = authors_years_wos.groupby(['authorId','publicationYear']).agg(list).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8f33f908",
   "metadata": {},
   "outputs": [],
   "source": [
    "authors_careers = authors_years_wos.drop_duplicates(subset='authorId', keep='first', ignore_index=True).rename(columns={'publicationYear':'beginYear','wos_domains_agg':'wos_begin'})\n",
    "authors_careers = pd.merge(authors_careers,\n",
    "                           authors_years_wos.drop_duplicates(subset='authorId', keep='last', ignore_index=True).rename(columns={'publicationYear':'lastYear','wos_domains_agg':'wos_last'}),\n",
    "                           on='authorId', how='left')\n",
    "authors_careers['deltaYear'] = [j-i for i,j in zip(authors_careers['beginYear'],authors_careers['lastYear'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "afdb7f29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BJndJapDJXZIaZHKXpAaZ3CWpQSZ3SWrQ/wJX7vNrvaFOZgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(authors_careers['deltaYear'], bins=50)\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "81f9490d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>authorId</th>\n",
       "      <th>beginYear</th>\n",
       "      <th>wos_begin</th>\n",
       "      <th>lastYear</th>\n",
       "      <th>wos_last</th>\n",
       "      <th>deltaYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18161</td>\n",
       "      <td>2008</td>\n",
       "      <td>[[Marine &amp; Freshwater Biology]]</td>\n",
       "      <td>2019</td>\n",
       "      <td>[[Environmental Sciences], [Marine &amp; Freshwate...</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20361</td>\n",
       "      <td>1999</td>\n",
       "      <td>[[Neurosciences]]</td>\n",
       "      <td>2004</td>\n",
       "      <td>[[Biochemistry &amp; Molecular Biology, Biophysics]]</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39506</td>\n",
       "      <td>2010</td>\n",
       "      <td>[[Neurosciences, Clinical Neurology, Gastroent...</td>\n",
       "      <td>2015</td>\n",
       "      <td>[[Cell Biology]]</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>57623</td>\n",
       "      <td>2005</td>\n",
       "      <td>[[Respiratory System]]</td>\n",
       "      <td>2009</td>\n",
       "      <td>[[Respiratory System, Physiology]]</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64128</td>\n",
       "      <td>2009</td>\n",
       "      <td>[[Neurosciences, Clinical Neurology], [Neurosc...</td>\n",
       "      <td>2018</td>\n",
       "      <td>[[Neurosciences, Behavioral Sciences]]</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290005</th>\n",
       "      <td>3035759232</td>\n",
       "      <td>2005</td>\n",
       "      <td>[[Neurosciences]]</td>\n",
       "      <td>2019</td>\n",
       "      <td>[-1]</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290006</th>\n",
       "      <td>3035760453</td>\n",
       "      <td>2012</td>\n",
       "      <td>[[Otorhinolaryngology]]</td>\n",
       "      <td>2018</td>\n",
       "      <td>[-1]</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290007</th>\n",
       "      <td>3035760604</td>\n",
       "      <td>2015</td>\n",
       "      <td>[[Neurosciences], [Neurosciences]]</td>\n",
       "      <td>2015</td>\n",
       "      <td>[[Neurosciences], [Neurosciences]]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290008</th>\n",
       "      <td>3035761758</td>\n",
       "      <td>2008</td>\n",
       "      <td>[[Neurosciences, Clinical Neurology]]</td>\n",
       "      <td>2018</td>\n",
       "      <td>[[Behavioral Sciences, Neurosciences]]</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290009</th>\n",
       "      <td>3035762904</td>\n",
       "      <td>1991</td>\n",
       "      <td>[[Evolutionary Biology, Ecology, Biology], [Bi...</td>\n",
       "      <td>2019</td>\n",
       "      <td>[[Neurosciences], [Evolutionary Biology, Ecolo...</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1290010 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           authorId  beginYear  \\\n",
       "0             18161       2008   \n",
       "1             20361       1999   \n",
       "2             39506       2010   \n",
       "3             57623       2005   \n",
       "4             64128       2009   \n",
       "...             ...        ...   \n",
       "1290005  3035759232       2005   \n",
       "1290006  3035760453       2012   \n",
       "1290007  3035760604       2015   \n",
       "1290008  3035761758       2008   \n",
       "1290009  3035762904       1991   \n",
       "\n",
       "                                                 wos_begin  lastYear  \\\n",
       "0                          [[Marine & Freshwater Biology]]      2019   \n",
       "1                                        [[Neurosciences]]      2004   \n",
       "2        [[Neurosciences, Clinical Neurology, Gastroent...      2015   \n",
       "3                                   [[Respiratory System]]      2009   \n",
       "4        [[Neurosciences, Clinical Neurology], [Neurosc...      2018   \n",
       "...                                                    ...       ...   \n",
       "1290005                                  [[Neurosciences]]      2019   \n",
       "1290006                            [[Otorhinolaryngology]]      2018   \n",
       "1290007                 [[Neurosciences], [Neurosciences]]      2015   \n",
       "1290008              [[Neurosciences, Clinical Neurology]]      2018   \n",
       "1290009  [[Evolutionary Biology, Ecology, Biology], [Bi...      2019   \n",
       "\n",
       "                                                  wos_last  deltaYear  \n",
       "0        [[Environmental Sciences], [Marine & Freshwate...         11  \n",
       "1         [[Biochemistry & Molecular Biology, Biophysics]]          5  \n",
       "2                                         [[Cell Biology]]          5  \n",
       "3                       [[Respiratory System, Physiology]]          4  \n",
       "4                   [[Neurosciences, Behavioral Sciences]]          9  \n",
       "...                                                    ...        ...  \n",
       "1290005                                               [-1]         14  \n",
       "1290006                                               [-1]          6  \n",
       "1290007                 [[Neurosciences], [Neurosciences]]          0  \n",
       "1290008             [[Behavioral Sciences, Neurosciences]]         10  \n",
       "1290009  [[Neurosciences], [Evolutionary Biology, Ecolo...         28  \n",
       "\n",
       "[1290010 rows x 6 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "authors_careers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f84dd997",
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge the careers with the original author file\n",
    "authorsInPapers = pd.merge(authorsInPapers, authors_careers, on='authorId', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "bec6aa58",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the file\n",
    "authorsInPapers.to_csv(f'{path_output}collabNet_nodes.csv', sep=';', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5991c8f2",
   "metadata": {},
   "source": [
    "## 4.3 - Build the parametrized temporal collaboration network\n",
    "Build a temporal multilayer network with AI and non-AI collaborations. Therefore two authors can be linked in different way, either 'AI' or 'nonAI' with time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "259788ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# NEUROAI\n",
    "cn = {}\n",
    "for y in tqdm(range(1970,2020)):\n",
    "    for authors in papersAI[papersAI['publicationYear']==y]['authors']:\n",
    "        if eval(authors) != -1:\n",
    "            a = list(set(eval(authors))) # to avoid the case where authors appear twice in a single publication\n",
    "            for i in range(len(a)-1):\n",
    "                for j in range(i+1,len(a)):\n",
    "                    if (a[i],a[j],y) in cn:\n",
    "                        cn[(a[i],a[j],y)][0] += 1\n",
    "                    elif (a[j],a[i],y) in cn:\n",
    "                        cn[(a[j],a[i],y)][0] += 1\n",
    "                    else:\n",
    "                        cn[(a[i],a[j],y)] = [1,0]\n",
    "print('NeuroAI ok')\n",
    "# NEURO\n",
    "for y in tqdm(set(papersNonAI['publicationYear'])):\n",
    "    for authors in papersNonAI[papersNonAI['publicationYear']==y]['authors']:\n",
    "        if eval(authors)!=-1:\n",
    "            a = list(set(eval(authors))) # to avoid the case where authors appear twice in a single publication\n",
    "            for i in range(len(a)-1):\n",
    "                for j in range(i+1,len(a)):\n",
    "                    if (a[i],a[j],y) in cn:\n",
    "                        cn[(a[i],a[j],y)][1] += 1\n",
    "                    elif (a[j],a[i],y) in cn:\n",
    "                        cn[(a[j],a[i],y)][1] += 1\n",
    "                    else:\n",
    "                        cn[(a[i],a[j],y)] = [0,1]\n",
    "print('Neuro ok')\n",
    "collabNet = [(i,j,y,k,l) for ((i,j,y),[k,l]) in cn.items()]\n",
    "collabNet = pd.DataFrame.from_records(collabNet, columns=['author1','author2','year','w_AI','w_nonAI'])\n",
    "collabNet['w'] = [i+j for i,j in zip(collabNet['w_nonAI'],collabNet['w_AI'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33044d1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# confirmation\n",
    "a1, a2, y = 2153041777, 20660026, 1973\n",
    "# avoid doublons\n",
    "print(a1 in list(collabNet[collabNet['author1']==a2]['author2']))\n",
    "# verify the variables 'w_AI' and 'w_nonAI'\n",
    "ids = set(allpapers[(allpapers['authors']==a1)]['id']) & set(allpapers[allpapers['authors']==a2]['id'])\n",
    "allpapers[(allpapers['id'].isin(ids)) \n",
    "          & (allpapers['publicationYear']==y)\n",
    "          & ((allpapers['authors']==a1)|(allpapers['authors']==a2))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6686856",
   "metadata": {},
   "outputs": [],
   "source": [
    "collabNet.to_csv(f'{path_output}collabNet.csv', sep=';', index=False)\n",
    "collabNet.rename(columns={'author1':'source','author2':'target'}).to_csv(f'{path_output}collabNet_edges_gephi.csv', sep=';', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1df04c14",
   "metadata": {},
   "source": [
    "## 4.5 - Build the time aggregated collaboration network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e6a8fafe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add weights\n",
    "def aggregate(temporal_collab_network):\n",
    "    cn = {}\n",
    "    for i,j,w in tqdm(temporal_collab_network[['author1','author2','w']].values):\n",
    "        if (i,j) in cn:\n",
    "            cn[(i,j)] += w\n",
    "        elif (j,i) in cn:\n",
    "            cn[(j,i)] += w\n",
    "        else:\n",
    "            cn[(i,j)] = w\n",
    "    cn = pd.DataFrame.from_records([(i,j,k) for ((i,j),k) in cn.items()], columns=['author1','author2','w'])\n",
    "    return cn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1074305",
   "metadata": {},
   "outputs": [],
   "source": [
    "collabNet_timeagg = aggregate(collabNet)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d6b0200",
   "metadata": {},
   "source": [
    "#### Verification by comparing to the weights of the original network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "cf06eb0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>author1</th>\n",
       "      <th>author2</th>\n",
       "      <th>w</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>331035</th>\n",
       "      <td>2737824110</td>\n",
       "      <td>2999599514</td>\n",
       "      <td>297</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           author1     author2    w\n",
       "331035  2737824110  2999599514  297"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind = np.where(collabNet_timeagg['w']==max(collabNet_timeagg['w']))\n",
    "collabNet_timeagg.loc[ind]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "9e07113b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "297"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(collabNet[((collabNet['author1']==2737824110) & (collabNet['author2']==2999599514))|\n",
    "              ((collabNet['author2']==2737824110) & (collabNet['author1']==2999599514))]['w'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2228d243",
   "metadata": {},
   "outputs": [],
   "source": [
    "collabNet_timeagg.to_csv(f'{path_output}collabNet_edges_timeagg.csv', sep=';', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d648a28f",
   "metadata": {},
   "source": [
    "# 5 - Comparison of different time-aggregated collaboration networks when applying different filters\n",
    "Compare:\n",
    "- their density, \n",
    "- their number of connected components and the distribution of their corresponding sizes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "68fb6f4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add weights\n",
    "def aggregate(temporal_collab_network):\n",
    "    cn = {}\n",
    "    for i,j,w in tqdm(temporal_collab_network[['author1','author2','w']].values):\n",
    "        if (i,j) in cn:\n",
    "            cn[(i,j)] += w\n",
    "        elif (j,i) in cn:\n",
    "            cn[(j,i)] += w\n",
    "        else:\n",
    "            cn[(i,j)] = w\n",
    "    cn = pd.DataFrame.from_records([(i,j,k) for ((i,j),k) in cn.items()], columns=['author1','author2','w'])\n",
    "    return cn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f2b262a",
   "metadata": {},
   "source": [
    "## 5.1 - Original time aggregated network with all the authors of the original database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "dd8883d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "collabNet = pd.read_csv(f'{path_output}collabNet.csv', sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d2acc006",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████| 12637315/12637315 [00:19<00:00, 644415.70it/s]\n"
     ]
    }
   ],
   "source": [
    "collabNet_timeagg = aggregate(collabNet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "628c2ac9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(collabNet_timeagg['w'],bins=100)\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "cbd91d46",
   "metadata": {},
   "outputs": [],
   "source": [
    "G = ig.Graph.DataFrame(collabNet_timeagg, directed=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "f716e9f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1278468"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(G.vs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "3a7f768b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10109269"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(G.es)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "81b891ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2370018924904797e-05"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G.density()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "069bf310",
   "metadata": {},
   "outputs": [],
   "source": [
    "comp = [c for c in G.components()]\n",
    "sizes = [len(c) for c in comp]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "e20a27f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[30, 31, 31, 32, 32, 32, 34, 39, 41, 1166044]"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(sizes)[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "76eea1a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(sizes,bins=100)\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7063c7f0",
   "metadata": {},
   "source": [
    "## 5.2 - Select only the authors having at least 3 publications"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "97ce2fe5",
   "metadata": {},
   "outputs": [],
   "source": [
    "authorsInPapers_sub = authorsInPapers[authorsInPapers['nb_publi_all']>=3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "ff9a5d4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "899706"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(authorsInPapers_sub)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "cc5a8234",
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_collabNet_timeagg = collabNet_timeagg[collabNet_timeagg['author1'].isin(authorsInPapers_sub['authorId'])\n",
    "                         & collabNet_timeagg['author2'].isin(authorsInPapers_sub['authorId'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "2940c94d",
   "metadata": {},
   "outputs": [],
   "source": [
    "G = ig.Graph.DataFrame(sub_collabNet_timeagg, directed=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "d85b9eb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "887227"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(G.vs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "ced3f5ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6876977012590096"
      ]
     },
     "execution_count": 350,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "887227/len(authorsInPapers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "aa794866",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7819355"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(G.es)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "e3328dea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.986698282284764e-05"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G.density()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "d4d9d513",
   "metadata": {},
   "outputs": [],
   "source": [
    "comp = [c for c in G.components()]\n",
    "sizes = [len(c) for c in comp]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "af22b717",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16561"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(comp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "66188a0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[24, 25, 25, 26, 27, 27, 28, 29, 30, 828240]"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(sizes)[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "bc990018",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(sizes,bins=100)\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "394e9fdf",
   "metadata": {},
   "source": [
    "## 5.3 - Select only the authors with bounded careers ($\\Delta t\\leqslant 50\\,years $ and $t_0\\geqslant 1940$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d033c1c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "authorsInPapers_sub = authorsInPapers[(authorsInPapers['deltaYear']<=50) & (authorsInPapers['beginYear']>=1940)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "bd411ccd",
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_collabNet_timeagg = collabNet_timeagg[collabNet_timeagg['author1'].isin(authorsInPapers_sub['authorId'])\n",
    "                                         & collabNet_timeagg['author2'].isin(authorsInPapers_sub['authorId'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10ab6e8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "G = ig.Graph.DataFrame(sub_collabNet_timeagg, directed=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "26b005aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1261059"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(G.vs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "92278678",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9641832"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(G.es)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "926b570f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2126042558122171e-05"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G.density()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "d7ddd253",
   "metadata": {},
   "outputs": [],
   "source": [
    "comp = [c for c in G.components()]\n",
    "sizes = [len(c) for c in comp]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "3db5d189",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32284"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(comp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "1397831b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[31, 32, 32, 32, 34, 34, 39, 41, 45, 1140463]"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(sizes)[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "0f6ffa00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(sizes,bins=100)\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "407a52a8",
   "metadata": {},
   "source": [
    "## 5.3 - Select only the authors with bounded careers ($\\Delta t\\leqslant 50\\,years $ and $t_0\\geqslant 1940$) and having at least 3 publications"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a84ea2ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "authorsInPapers_sub = authorsInPapers[(authorsInPapers['nb_publi_all']>=3) &\n",
    "                                      (authorsInPapers['deltaYear']<=50) &\n",
    "                                      (authorsInPapers['beginYear']>=1940)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5742ade1",
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_collabNet_timeagg = collabNet_timeagg[collabNet_timeagg['author1'].isin(authorsInPapers_sub['authorId'])\n",
    "                         & collabNet_timeagg['author2'].isin(authorsInPapers_sub['authorId'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "0f3d679d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(sub_collabNet_timeagg['w'],bins=100)\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "6e135adc",
   "metadata": {},
   "outputs": [],
   "source": [
    "G = ig.Graph.DataFrame(sub_collabNet_timeagg, directed=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "7b5a4694",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "871282"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(G.vs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "2c895fcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7420423"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(G.es)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "a3f2b108",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10785916"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(sub_collabNet_timeagg['w'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "f876e02b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6753385870226588"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(G.vs)/len(authorsInPapers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "id": "534be886",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2176531209689165"
      ]
     },
     "execution_count": 407,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# share of removing edges\n",
    "(sum(collabNet_timeagg['w'])-sum(sub_collabNet_timeagg['w']))/sum(collabNet_timeagg['w'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "id": "579ab0ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.9549770933834205e-05"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G.density()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "22ca445c",
   "metadata": {},
   "outputs": [],
   "source": [
    "comp = [c for c in G.components()]\n",
    "sizes = sorted([len(c) for c in comp])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "053a3e77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8799, 5840, 5839, ..., 7410, 1192,    0])"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "19127fdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17599"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(comp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "id": "1d29a70f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[25, 26, 27, 27, 27, 28, 28, 29, 30, 809280]"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(sizes)[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "65aefee7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62002"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(sizes[:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "id": "2816197f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07661378015025702"
      ]
     },
     "execution_count": 408,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# share of nodes in independent connected components, here 7.7%\n",
    "62002/809280"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "fd5f6d62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9288381947521009"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sizes[-1]/len(G.vs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "8ee1a841",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(sizes,bins=100)\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6856ad29",
   "metadata": {},
   "source": [
    "## 5.4 - Who are the problematic persons? \n",
    "Compare with the percentage of persons making AI wrt to the `AI_score` vs `nb_publi_all`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "876e3a9d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>authorId</th>\n",
       "      <th>name</th>\n",
       "      <th>affiliation</th>\n",
       "      <th>queryLevel</th>\n",
       "      <th>nb_publi_neuro</th>\n",
       "      <th>AIscore_neuro</th>\n",
       "      <th>nb_publi_all</th>\n",
       "      <th>AIscore_all</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2806045187</td>\n",
       "      <td>A.A.A. Pradhan</td>\n",
       "      <td>5023651</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2974182613</td>\n",
       "      <td>A. A. Aarnisalo</td>\n",
       "      <td>NONE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2840631567</td>\n",
       "      <td>A A Abdel-Rahman</td>\n",
       "      <td>1309191912</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2639413062</td>\n",
       "      <td>A.A. Acheampong</td>\n",
       "      <td>201448701</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2437395452</td>\n",
       "      <td>A A Ajayi</td>\n",
       "      <td>45711476</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290136</th>\n",
       "      <td>2263707641</td>\n",
       "      <td>Е. Viteva</td>\n",
       "      <td>NONE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290137</th>\n",
       "      <td>3022633000</td>\n",
       "      <td>Е Е Garanina</td>\n",
       "      <td>21203515</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290138</th>\n",
       "      <td>2688406403</td>\n",
       "      <td>Мikhail Pashenkov</td>\n",
       "      <td>NONE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290139</th>\n",
       "      <td>2495571327</td>\n",
       "      <td>І. L. Popovych</td>\n",
       "      <td>149851306</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290140</th>\n",
       "      <td>2732206803</td>\n",
       "      <td>聡 宇多</td>\n",
       "      <td>42766147</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>404067 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           authorId               name affiliation  queryLevel  \\\n",
       "2        2806045187     A.A.A. Pradhan     5023651         0.0   \n",
       "3        2974182613    A. A. Aarnisalo        NONE         0.0   \n",
       "5        2840631567   A A Abdel-Rahman  1309191912         0.0   \n",
       "6        2639413062    A.A. Acheampong   201448701         0.0   \n",
       "8        2437395452          A A Ajayi    45711476         0.0   \n",
       "...             ...                ...         ...         ...   \n",
       "1290136  2263707641          Е. Viteva        NONE         0.0   \n",
       "1290137  3022633000       Е Е Garanina    21203515         0.0   \n",
       "1290138  2688406403  Мikhail Pashenkov        NONE         0.0   \n",
       "1290139  2495571327     І. L. Popovych   149851306         0.0   \n",
       "1290140  2732206803               聡 宇多    42766147         0.0   \n",
       "\n",
       "         nb_publi_neuro  AIscore_neuro  nb_publi_all  AIscore_all  \n",
       "2                   1.0            0.0             1          0.0  \n",
       "3                   1.0            0.0             2          0.0  \n",
       "5                   1.0            0.0             1          0.0  \n",
       "6                   1.0            1.0             1          1.0  \n",
       "8                   1.0            0.0             1          0.0  \n",
       "...                 ...            ...           ...          ...  \n",
       "1290136             1.0            0.0             1          0.0  \n",
       "1290137             1.0            0.0             1          0.0  \n",
       "1290138             1.0            0.0             1          0.0  \n",
       "1290139             1.0            0.0             1          0.0  \n",
       "1290140             1.0            0.0             1          0.0  \n",
       "\n",
       "[404067 rows x 8 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "authorsInPapers_reject = authorsInPapers[~((authorsInPapers['nb_publi_all']>=3) &\n",
    "                                           (authorsInPapers['deltaYear']<=50))\n",
    "                                           (authorsInPapers['beginYear']>=1940)]\n",
    "authorsInPapers_reject"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "52763197",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.31319599950703064"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(authorsInPapers_reject)/len(authorsInPapers)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31085d79",
   "metadata": {},
   "source": [
    "### 5.4.1 - Study of the AI score of the rejected authors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "3175a1ea",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5, 3))\n",
    "ax = plt.axes()\n",
    "ax.hist(authorsInPapers_reject['AIscore_all'],bins=50)\n",
    "ax.set_xlabel(r'$f_{AI}$', fontsize=axisLabelSize-3)\n",
    "ax.set_ylabel(r'Frequency', fontsize=axisLabelSize-3)\n",
    "ax.set_yscale('log')\n",
    "ax.tick_params(which='major', labelsize=ticksLabelSize, length=7, width=ticksWidth)\n",
    "ax.tick_params(which='minor', length=4, width=ticksWidth)\n",
    "plt.savefig(f'{path_output}AIscore_distrib_RejectedAuthors.pdf', format='pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "1b39e069",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15763935879876695"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(authorsInPapers[authorsInPapers['AIscore_all']>0])/len(authorsInPapers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "cdcd674d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.02559971489876679"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fraction of rejected authors who are considered as AI-expert\n",
    "len(authorsInPapers_reject[authorsInPapers_reject['AIscore_all']>=0.5])/len(authorsInPapers_reject)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "47dd2d74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0740103354853302"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loss of AI practitioners compared to the original authors' database above\n",
    "len(authorsInPapers_reject[authorsInPapers_reject['AIscore_all']>0])/len(authorsInPapers[authorsInPapers['AIscore_all']>0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "3c635334",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6570121951219512"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loss of AI expert authors compared to the original authors' database above\n",
    "len(authorsInPapers_reject[authorsInPapers_reject['AIscore_all']>=0.5])/len(authorsInPapers[authorsInPapers['AIscore_all']>=0.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "id": "376f0a1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.945664739884393"
      ]
     },
     "execution_count": 409,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# share of authors having AIscore_all=1 in the rejected wrt to the whole set\n",
    "len(authorsInPapers_reject[authorsInPapers_reject['AIscore_all']==1])/len(authorsInPapers[authorsInPapers['AIscore_all']==1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fadd4d88",
   "metadata": {},
   "source": [
    "### 5.4.2 - Study of the beginning year of the career"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "68c3ca67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5, 3))\n",
    "ax = plt.axes()\n",
    "ax.hist(authorsInPapers_reject['beginYear'],bins=50)\n",
    "ax.set_xlabel(r'Begin year', fontsize=axisLabelSize-3)\n",
    "ax.set_ylabel(r'Frequency', fontsize=axisLabelSize-3)\n",
    "ax.set_yscale('log')\n",
    "ax.tick_params(which='major', labelsize=ticksLabelSize, length=7, width=ticksWidth)\n",
    "ax.tick_params(which='minor', length=4, width=ticksWidth)\n",
    "plt.savefig(f'{path_output}beginYear_distrib_RejectedAuthors.pdf', format='pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "9e594663",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9947558201981355"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(authorsInPapers_reject[authorsInPapers_reject['beginYear']>=1940])/len(authorsInPapers_reject)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26878a9b",
   "metadata": {},
   "source": [
    "### 5.4.2 - Study of the loss of links in the collaboration network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "73653b0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_collabNet_timeagg_rejected = collabNet_timeagg[collabNet_timeagg['author1'].isin(authorsInPapers_reject['authorId'])|\n",
    "                                                   collabNet_timeagg['author2'].isin(authorsInPapers_reject['authorId'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "0fc3d29c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2782053930329144"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_collabNet_timeagg_rejected['w'].sum()/sub_collabNet_timeagg['w'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "500a3cfa",
   "metadata": {},
   "source": [
    "Results: removing the problematic researchers from the whole collaboration network rejects 27.8% of the edges and 31.3% of the authors. \n",
    "According to the results below, 15% of them have published at least one AI-related article. \n",
    "If we consider only the AI practitioners of the original database, 7.5% of them are rejected by the filtration.\n",
    "If we consider the rather AI-experts ($f_{AI}\\geqslant 0.5$), 65.7% of them are rejected by the filtration, who are at most young researchers, or scientists publishing episodically on traditional academic media such as peer-reviewed journals here, or scientists publishing not regularly in such journals (since $n_{publi} \\leqslant 2$).\n",
    "Moreover, givent their low number of publications in our database, they therefore don't represent a significant part of the collboration network, with a small ego-network.\n",
    "In this way we conserve the authors with significant academic expience through the publications in the academic journals chosen to represent the neuroscience field, and who have respectively an ego-collaboration network that could be more extended (wider)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bed7bbda",
   "metadata": {},
   "source": [
    "### 5.4.3 - Check the potential loss within the time-aggregated collaboration network of AI research (ie. within $\\mathcal{P}\\cap AI$) if we remove the problematic authors in it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f657c932",
   "metadata": {},
   "outputs": [],
   "source": [
    "papersAI['authors'] = papersAI['authors'].apply(lambda x: eval(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "1467f46c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8643376060703984"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# share of authors in the subset having complete carrer and at least 3 publications\n",
    "len(set(papersAI.explode('authors')['authors'])&set(authorsInPapers_sub['authorId']))/len(set(papersAI.explode('authors')['authors']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "67a5eba3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>authorId</th>\n",
       "      <th>name</th>\n",
       "      <th>affiliation</th>\n",
       "      <th>queryLevel</th>\n",
       "      <th>nb_publi_neuro</th>\n",
       "      <th>AIscore_neuro</th>\n",
       "      <th>nb_publi_all</th>\n",
       "      <th>AIscore_all</th>\n",
       "      <th>beginYear</th>\n",
       "      <th>wos_begin</th>\n",
       "      <th>lastYear</th>\n",
       "      <th>wos_last</th>\n",
       "      <th>deltaYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2639413062</td>\n",
       "      <td>A.A. Acheampong</td>\n",
       "      <td>201448701</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>[['Neurosciences', 'Pharmacology &amp; Pharmacy']]</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>[['Neurosciences', 'Pharmacology &amp; Pharmacy']]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2699497383</td>\n",
       "      <td>A A Barnes</td>\n",
       "      <td>188950975</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>[['Neurosciences', 'Clinical Neurology', 'Gast...</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>[['Neurosciences', 'Clinical Neurology', 'Gast...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>2488445124</td>\n",
       "      <td>A A Kruglov</td>\n",
       "      <td>32389192</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>[['Cell Biology']]</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>[['Cell Biology']]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>2974365865</td>\n",
       "      <td>A. Agogino</td>\n",
       "      <td>86519309</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>[['Computer Science']]</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>[['Computer Science']]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>2495994358</td>\n",
       "      <td>A Al-Mohammed</td>\n",
       "      <td>NONE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>[['Ophthalmology']]</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>[['Ophthalmology']]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1289831</th>\n",
       "      <td>2536406627</td>\n",
       "      <td>Zvenislava Husak</td>\n",
       "      <td>NONE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>[['Cell Biology', 'Biochemistry &amp; Molecular Bi...</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>[['Cell Biology', 'Biochemistry &amp; Molecular Bi...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1289888</th>\n",
       "      <td>2770849496</td>\n",
       "      <td>Zvi Vogel</td>\n",
       "      <td>NONE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.025</td>\n",
       "      <td>190</td>\n",
       "      <td>0.005263</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>[['Multidisciplinary Sciences'], ['Biochemistr...</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>[['Multidisciplinary Sciences']]</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290052</th>\n",
       "      <td>2120994696</td>\n",
       "      <td>Şehri Kılıç</td>\n",
       "      <td>67581229</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>[['Neurosciences']]</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>[['Neurosciences']]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290079</th>\n",
       "      <td>2276486861</td>\n",
       "      <td>Ştefan Mihalaş</td>\n",
       "      <td>145311948</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>[['Neurosciences', 'Computer Science']]</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>[['Neurosciences', 'Computer Science']]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290130</th>\n",
       "      <td>2783816921</td>\n",
       "      <td>Κ Dattani-Pitt</td>\n",
       "      <td>NONE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>[-1]</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>[-1]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11334 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           authorId              name affiliation  queryLevel  nb_publi_neuro  \\\n",
       "6        2639413062   A.A. Acheampong   201448701         0.0             1.0   \n",
       "34       2699497383        A A Barnes   188950975         0.0             1.0   \n",
       "210      2488445124       A A Kruglov    32389192         0.0             1.0   \n",
       "510      2974365865        A. Agogino    86519309         0.0             1.0   \n",
       "655      2495994358     A Al-Mohammed        NONE         0.0             1.0   \n",
       "...             ...               ...         ...         ...             ...   \n",
       "1289831  2536406627  Zvenislava Husak        NONE         0.0             1.0   \n",
       "1289888  2770849496         Zvi Vogel        NONE         0.0            40.0   \n",
       "1290052  2120994696       Şehri Kılıç    67581229         0.0             1.0   \n",
       "1290079  2276486861    Ştefan Mihalaş   145311948         0.0             1.0   \n",
       "1290130  2783816921    Κ Dattani-Pitt        NONE         0.0             1.0   \n",
       "\n",
       "         AIscore_neuro  nb_publi_all  AIscore_all  beginYear  \\\n",
       "6                1.000             1     1.000000     1988.0   \n",
       "34               1.000             1     1.000000     2009.0   \n",
       "210              1.000             1     1.000000     2014.0   \n",
       "510              1.000             1     1.000000     2000.0   \n",
       "655              1.000             1     1.000000     2005.0   \n",
       "...                ...           ...          ...        ...   \n",
       "1289831          1.000             1     1.000000     2001.0   \n",
       "1289888          0.025           190     0.005263     1968.0   \n",
       "1290052          1.000             1     1.000000     2001.0   \n",
       "1290079          1.000             1     1.000000     2009.0   \n",
       "1290130          1.000             1     1.000000     2005.0   \n",
       "\n",
       "                                                 wos_begin  lastYear  \\\n",
       "6           [['Neurosciences', 'Pharmacology & Pharmacy']]    1988.0   \n",
       "34       [['Neurosciences', 'Clinical Neurology', 'Gast...    2009.0   \n",
       "210                                     [['Cell Biology']]    2014.0   \n",
       "510                                 [['Computer Science']]    2000.0   \n",
       "655                                    [['Ophthalmology']]    2005.0   \n",
       "...                                                    ...       ...   \n",
       "1289831  [['Cell Biology', 'Biochemistry & Molecular Bi...    2001.0   \n",
       "1289888  [['Multidisciplinary Sciences'], ['Biochemistr...    2019.0   \n",
       "1290052                                [['Neurosciences']]    2001.0   \n",
       "1290079            [['Neurosciences', 'Computer Science']]    2009.0   \n",
       "1290130                                               [-1]    2005.0   \n",
       "\n",
       "                                                  wos_last  deltaYear  \n",
       "6           [['Neurosciences', 'Pharmacology & Pharmacy']]        0.0  \n",
       "34       [['Neurosciences', 'Clinical Neurology', 'Gast...        0.0  \n",
       "210                                     [['Cell Biology']]        0.0  \n",
       "510                                 [['Computer Science']]        0.0  \n",
       "655                                    [['Ophthalmology']]        0.0  \n",
       "...                                                    ...        ...  \n",
       "1289831  [['Cell Biology', 'Biochemistry & Molecular Bi...        0.0  \n",
       "1289888                   [['Multidisciplinary Sciences']]       51.0  \n",
       "1290052                                [['Neurosciences']]        0.0  \n",
       "1290079            [['Neurosciences', 'Computer Science']]        0.0  \n",
       "1290130                                               [-1]        0.0  \n",
       "\n",
       "[11334 rows x 13 columns]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# who are the rejected ones?\n",
    "authorsNeuroAI_rejected_ids = set(papersAI.explode('authors')['authors'])-(set(papersAI.explode('authors')['authors'])&set(authorsInPapers_sub['authorId']))\n",
    "authorsNeuroAI_rejected = authorsInPapers[authorsInPapers['authorId'].isin(authorsNeuroAI_rejected_ids)]\n",
    "authorsNeuroAI_rejected"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faa6d757",
   "metadata": {},
   "source": [
    "#### 5.4.3.1 - Distribution of the career durations of the authors in $\\mathcal{P}\\cap AI$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "fb2e58e6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XixYwMN71SEsUPsjvu7aCtyX1CV+0IYCBBrkV+1Q63bTxe6WFfbq4LpasRAcEMNDs1sgKWXuSerbvNl1se9v2ge0DpRbwen6/fxmFxWKiDxgYkVu/aw3LGB4orS38fstnX0jaDHbNQAtawMBZo63fgV2lVvCkrohtiS2L0A0BDAzJ29v8relcfqj2RKk7Yhz6f9EZAQy8bScinkw4/0CTW8Fbyg/vgDYEMJDl1u/EoWO5a+FQqTui6fM90QJGRwQw8EZb63dgV2mUw87I8TP9v6wjgUkIYECnGz/2u1ybA/ZIZ/uCN/PxwT13lGbSAY3erV0AYE7cl/Q4DyPr4rGkPdvbIyMmjqXTQF/v2KLGimIcMFZey7bnbY4iYjPfZ13Svt7MoPtmJgXE0iKAAaAS+oABoBICGAAqIYABoBICGAAqIYABoBICGAAqIYABoBICGAAqIYABoBICGAAq+X/7tQI/CZkdxQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5, 3))\n",
    "ax = plt.axes()\n",
    "ax.hist(authorsNeuroAI_rejected['deltaYear'],bins=50)\n",
    "ax.set_xlabel(r'$\\Delta t$', fontsize=axisLabelSize-3)\n",
    "ax.set_ylabel(r'Frequency', fontsize=axisLabelSize-3)\n",
    "ax.set_yscale('log')\n",
    "ax.tick_params(which='major', labelsize=ticksLabelSize, length=7, width=ticksWidth)\n",
    "ax.tick_params(which='minor', length=4, width=ticksWidth)\n",
    "plt.savefig(f'{path_output}Deltayear_distrib_RejectedAuthorsNeuroAI_ONLY.pdf', format='pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "506765ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.833950944062114"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fraction of the authors having a career duration lower than or equal to 3\n",
    "1-len(authorsNeuroAI_rejected[authorsNeuroAI_rejected['deltaYear']>3])/len(authorsNeuroAI_rejected)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80ea8859",
   "metadata": {},
   "source": [
    "#### 5.4.3.2 - Distribution of the publication years of their articles in  $\\mathcal{P}\\cap AI$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6abfbde1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# when are situated their main publications?\n",
    "p = papersAI.explode('authors')\n",
    "publis = p[p['authors'].isin(authorsNeuroAI_rejected['authorId'])].drop_duplicates('id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "39477944",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.32623037840297264"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(publis)/len(papersAI['id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "5502cfad",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5, 3))\n",
    "ax = plt.axes()\n",
    "ax.hist(publis['publicationYear'],bins=50)\n",
    "ax.set_xlabel(r'$N_{publi}$', fontsize=axisLabelSize-3)\n",
    "ax.set_ylabel(r'$t$', fontsize=axisLabelSize-3)\n",
    "#ax.set_yscale('log')\n",
    "ax.tick_params(which='major', labelsize=ticksLabelSize, length=7, width=ticksWidth)\n",
    "#ax.tick_params(which='minor', length=4, width=ticksWidth)\n",
    "plt.savefig(f'{path_output}NbPublis_distrib_RejectedAuthorsNeuroAI_ONLY.pdf', format='pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7692729b",
   "metadata": {},
   "source": [
    "#### 5.4.3.3 - Loss in the collaboration network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "ce3b0c30",
   "metadata": {},
   "outputs": [],
   "source": [
    "# study the sub collaboration network in AI\n",
    "cn = {}\n",
    "for authors in papersAI['authors']:\n",
    "    if authors != -1:\n",
    "        a = list(set(authors)) # to avoid the case where authors appear twice in a single publication\n",
    "        for i in range(len(a)-1):\n",
    "            for j in range(i+1,len(a)):\n",
    "                if (a[i],a[j]) in cn:\n",
    "                    cn[(a[i],a[j])] += 1\n",
    "                elif (a[j],a[i]) in cn:\n",
    "                    cn[(a[j],a[i])] += 1\n",
    "                else:\n",
    "                    cn[(a[i],a[j])] = 1\n",
    "collabNet_NeuroAI = [(i,j,k) for ((i,j),k) in cn.items()]\n",
    "collabNet_NeuroAI = pd.DataFrame.from_records(collabNet_NeuroAI, columns=['author1','author2','w'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "ff23ea40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>author1</th>\n",
       "      <th>author2</th>\n",
       "      <th>w</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3024761849</td>\n",
       "      <td>2619442247</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2145260682</td>\n",
       "      <td>2835622326</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2995834608</td>\n",
       "      <td>2891704168</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2995834608</td>\n",
       "      <td>2798335766</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2995834608</td>\n",
       "      <td>2651595071</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379002</th>\n",
       "      <td>712055444</td>\n",
       "      <td>2405690839</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379003</th>\n",
       "      <td>712055444</td>\n",
       "      <td>2102785432</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379004</th>\n",
       "      <td>2905290646</td>\n",
       "      <td>2405690839</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379005</th>\n",
       "      <td>2905290646</td>\n",
       "      <td>2102785432</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379006</th>\n",
       "      <td>2405690839</td>\n",
       "      <td>2102785432</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>379007 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           author1     author2  w\n",
       "0       3024761849  2619442247  1\n",
       "1       2145260682  2835622326  1\n",
       "2       2995834608  2891704168  2\n",
       "3       2995834608  2798335766  3\n",
       "4       2995834608  2651595071  2\n",
       "...            ...         ... ..\n",
       "379002   712055444  2405690839  1\n",
       "379003   712055444  2102785432  1\n",
       "379004  2905290646  2405690839  1\n",
       "379005  2905290646  2102785432  1\n",
       "379006  2405690839  2102785432  1\n",
       "\n",
       "[379007 rows x 3 columns]"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collabNet_NeuroAI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "0f1083ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8302825444655331"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# share of total links of the subgraph of AI research if we remove problematic authors publishing in it\n",
    "sum(collabNet_NeuroAI[collabNet_NeuroAI['author1'].isin(authorsInPapers_sub['authorId'])&\n",
    "                      collabNet_NeuroAI['author2'].isin(authorsInPapers_sub['authorId'])]['w'])/sum(collabNet_NeuroAI['w'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "ba4e7724",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00075769137256017"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# and among the whole collaboration network of the original database\n",
    "sum(collabNet_timeagg[collabNet_timeagg['author1'].isin(authorsNeuroAI_rejected['authorId']) &\n",
    "                      collabNet_timeagg['author2'].isin(authorsNeuroAI_rejected['authorId'])]['w'])/sum(collabNet_timeagg['w'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b45f8df2",
   "metadata": {},
   "source": [
    "Results: the authors that have been removed in this little subset of authors in $\\mathcal{P}\\cap AI$ represent 14% of the subset of authors over only 33% of the publications of $\\mathcal{P}\\cap AI$. \n",
    "\n",
    "Removing these authors from the sub collaboration network of $\\mathcal{P}\\cap AI$ conserves 83% of its links.\n",
    "The authors that have been removed in this little subset of authors in $\\mathcal{P}\\cap AI$ are thus not much driving the whole AI research in neuroscience.\n",
    "Removing them from the whole unfiltered collaboration network of $\\mathcal{P}\\cap AI$ produces a loss of 0.08% of its links.\n",
    "Their contributions in the original dataset is thus unsignificant."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4fdacde",
   "metadata": {},
   "source": [
    "# 6 - Quantify the loss of authors w.r.t the original dataset and the loss of AI authors doing AI\n",
    "Compare the collaboration network a different steps of filtration, following the different sections, and with several indicators:\n",
    "- Degree distribution\n",
    "- The number of connected components\n",
    "- The global clustering coefficient of their nodes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "988c054d",
   "metadata": {},
   "source": [
    "## 6.1 - Original database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2ae2d6e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_248177/1599888032.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  papers_original_Ql0['authors'] = papers_original_Ql0['authors'].apply(lambda x: eval(x))\n"
     ]
    }
   ],
   "source": [
    "papers_original = pd.read_csv(f'{path_output}citationNetwork_nodes.csv', sep=';')\n",
    "papers_original_Ql0 = papers_original[(papers_original['queryLevel']==0)\n",
    "                                     & (papers_original['publicationYear']>=1970)\n",
    "                                     & (papers_original['publicationYear']<=2020)]\n",
    "papers_original_Ql0['authors'] = papers_original_Ql0['authors'].apply(lambda x: eval(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "645c37e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "allAuthors_original_Ql0 = papers_original_Ql0['authors']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "11cbcf6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2680215"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of unique authors in the original database \n",
    "len(set(allAuthors_original_Ql0.explode())-{-1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "451ac8ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████| 2052292/2052292 [00:43<00:00, 47383.47it/s]\n"
     ]
    }
   ],
   "source": [
    "# build the original time-aggregated collaboration network\n",
    "cn_original = {}\n",
    "for p in tqdm(allAuthors_original_Ql0):\n",
    "    if p!=-1:\n",
    "        a = list(set(p)) # to avoid the case where authors appear twice in a single publication\n",
    "        for i in range(len(a)-1):\n",
    "            for j in range(i+1,len(a)):\n",
    "                if (a[i],a[j]) in cn_original:\n",
    "                    cn_original[(a[i],a[j])] += 1\n",
    "                elif (a[j],a[i]) in cn_original:\n",
    "                    cn_original[(a[j],a[i])] += 1\n",
    "                else:\n",
    "                    cn_original[(a[i],a[j])] = 1\n",
    "cn_original = pd.DataFrame([(i,j,w) for (i,j),w in cn_original.items()], columns=['a1','a2','weight'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6f5adc3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "G_original = nx.from_pandas_edgelist(cn_original, source='a1', target='a2', edge_attr='weight', create_using=nx.Graph, edge_key=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a3f47372",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2611456"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(G_original.nodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "77eed804",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37354666"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total number of edges, summing the weights of the network\n",
    "w = nx.get_edge_attributes(G_original,'weight')\n",
    "sum(w.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5a83a1cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.025654285197269623"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(len(set(allAuthors_original_Ql0.explode())-{-1}) - len(G_original.nodes))/len(set(allAuthors_original_Ql0.explode())-{-1})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a28ce12",
   "metadata": {},
   "source": [
    "Result: We count only 2.6% of authors who have published alone in the original dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "56863858",
   "metadata": {},
   "outputs": [],
   "source": [
    "# distribution of the size of the connected components\n",
    "c = [i for i in nx.connected_components(G_original)]\n",
    "lenc = [len(i) for i in c]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4198eea8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95635"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(lenc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bf968ba7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.13667586204783844"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(sum(lenc)-max(lenc))/sum(lenc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79be757a",
   "metadata": {},
   "source": [
    "Results: the giant component of the collaboration network, without the alone authors, concentrates 86% of all the authors of the dataset, the other 14% being distributed among 95.634 smaller components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0278c69f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# degree distribution\n",
    "degree_freq = nx.degree_histogram(G_original)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e51f5f8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# weighted degree distribution\n",
    "degree_freq_weighted = Counter(dict(G_original.degree(weight='weight')).values())\n",
    "degree_freq_weighted = {k:degree_freq_weighted[k] for k in sorted(degree_freq_weighted.keys())}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3cfefec0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# clustering coefficient only on the giant component\n",
    "clustering_coeff = nx.average_clustering(G_original, nodes=c[0], weight='weight', count_zeros=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ccd6b6bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0013347462600322914"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clustering_coeff"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0600c4b",
   "metadata": {},
   "source": [
    "## 6.2 - Collaboration network from the filtered databases $\\mathcal{P}\\cap AI$ and $\\mathcal{P}\\cap \\overline{AI}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1fbcf3aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████| 12637315/12637315 [00:18<00:00, 699367.22it/s]\n"
     ]
    }
   ],
   "source": [
    "authorsInPapers = pd.read_csv(f'{path_output}collabNet_nodes.csv', sep=';')\n",
    "collabNet = pd.read_csv(f'{path_output}collabNet.csv', sep=';')\n",
    "collabNet_timeagg = aggregate(collabNet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9954e6bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "G_filtered = nx.from_pandas_edgelist(collabNet_timeagg, source='author1', target='author2', edge_attr='w', create_using=nx.Graph, edge_key=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a43f0a0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1278468 13786616\n"
     ]
    }
   ],
   "source": [
    "print(len(G_filtered.nodes), sum(nx.get_edge_attributes(G_filtered,'w').values()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d22e4421",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00904784825844617"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(1290141-len(G_filtered.nodes))/1290141"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2679f6d",
   "metadata": {},
   "source": [
    "Result: 1% of the authors of the filtered database are publishing alone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4562a178",
   "metadata": {},
   "outputs": [],
   "source": [
    "idAI_filtered = authorsInPapers[(authorsInPapers['authorId'].isin(G_filtered.nodes)) & (authorsInPapers['AIscore_all']>0)]['authorId']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "5d7e4a38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8253558"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = collabNet_timeagg[(collabNet_timeagg['author1'].isin(idAI_filtered))|(collabNet_timeagg['author2'].isin(idAI_filtered))]\n",
    "sum(l['w'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "dcdf3830",
   "metadata": {},
   "outputs": [],
   "source": [
    "# internal graph between only the AI practioners and \n",
    "G_sub = G_filtered.subgraph(nodes=list(idAI_filtered))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0e8785f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3136119"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(nx.get_edge_attributes(G_sub,'w').values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e333c53c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# distribution of the size of the connected components\n",
    "c = [i for i in nx.connected_components(G_filtered)]\n",
    "lenc = [len(i) for i in c]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "dbc1f449",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29618"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(lenc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f1ecd572",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08793649899723732"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(sum(lenc)-max(lenc))/sum(lenc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8bef032",
   "metadata": {},
   "source": [
    "Results: the giant component of the filtered collaboration network, without the alone authors, concentrates 91% of all the authors of the dataset, the other 9% being distributed among 29617 smaller components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "96e233f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# degree distribution\n",
    "degree_freq_filtered = nx.degree_histogram(G_filtered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a2ea243c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# weighted degree distribution\n",
    "degree_freq_filtered_weighted = Counter(dict(G_filtered.degree(weight='w')).values())\n",
    "degree_freq_filtered_weighted = {k:degree_freq_filtered_weighted[k] for k in sorted(degree_freq_filtered_weighted.keys())}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "04ed5e11",
   "metadata": {},
   "outputs": [],
   "source": [
    "# clustering coefficient only on the giant component\n",
    "clustering_coeff_filtered = nx.average_clustering(G_filtered, nodes=c[0], weight='w', count_zeros=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "8317010c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0034082006306942595"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clustering_coeff_filtered"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64420f27",
   "metadata": {},
   "source": [
    "## 6.3 - Filtered collaboration network considering only authors with careers length lower than 50 years and a minimal number of publications set at 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1397df56",
   "metadata": {},
   "outputs": [],
   "source": [
    "authorsInPapers_sub = authorsInPapers[(authorsInPapers['nb_publi_all']>=3) &\n",
    "                                      (authorsInPapers['deltaYear']<=50) &\n",
    "                                      (authorsInPapers['beginYear']>=1940)]\n",
    "sub_collabNet = collabNet[collabNet['author1'].isin(authorsInPapers_sub['authorId'])\n",
    "                         & collabNet['author2'].isin(authorsInPapers_sub['authorId'])]\n",
    "sub_collabNet_timeagg = collabNet_timeagg[collabNet_timeagg['author1'].isin(authorsInPapers_sub['authorId'])\n",
    "                                         & collabNet_timeagg['author2'].isin(authorsInPapers_sub['authorId'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8d4d1e2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.929246899382491"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(sub_collabNet[sub_collabNet['year']>=1998]['w'])/sum(sub_collabNet['w'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "96ab841f",
   "metadata": {},
   "outputs": [],
   "source": [
    "G_filtered_bis = nx.from_pandas_edgelist(sub_collabNet_timeagg, source='author1', target='author2', edge_attr='w', create_using=nx.Graph, edge_key=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8509d6df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "871282 10785916\n"
     ]
    }
   ],
   "source": [
    "print(len(G_filtered_bis.nodes), sum(nx.get_edge_attributes(G_filtered_bis,'w').values()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e0369544",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.016693865297932226"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(886074-len(G_filtered_bis.nodes))/886074"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "275af179",
   "metadata": {},
   "source": [
    "Result: 1,7% of the authors of the filtered database are publishing alone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "11a346ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# distribution of the size of the connected components\n",
    "c = [i for i in nx.connected_components(G_filtered_bis)]\n",
    "lenc = [len(i) for i in c]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2771a6aa",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17599"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(lenc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cd5ef9cd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07116180524789907"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(sum(lenc)-max(lenc))/sum(lenc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6268233e",
   "metadata": {},
   "outputs": [],
   "source": [
    "idAI_filtered_bis = authorsInPapers[(authorsInPapers['authorId'].isin(G_filtered_bis.nodes)) & (authorsInPapers['AIscore_all']>0)]['authorId']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "6b9f87d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4531580 6960244\n"
     ]
    }
   ],
   "source": [
    "l = sub_collabNet_timeagg[(sub_collabNet_timeagg['author1'].isin(idAI_filtered_bis))|(sub_collabNet_timeagg['author2'].isin(idAI_filtered_bis))]\n",
    "print(len(l), sum(l['w']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "bb9359ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# internal graph between only the AI practioners and \n",
    "G_sub = G_filtered.subgraph(nodes=list(idAI_filtered_bis))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "cd181d36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2839313"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(nx.get_edge_attributes(G_sub,'w').values())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4a2bf0b",
   "metadata": {},
   "source": [
    "Results: the giant component of the filtered collaboration network, without the alone authors, concentrates 93% of all the authors of the dataset, the other 7% being distributed among 17598 smaller components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "faa6aec6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# degree distribution\n",
    "degree_freq_filtered_bis = nx.degree_histogram(G_filtered_bis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "de92a0ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# weighted degree distribution\n",
    "degree_freq_filtered_bis_weighted = Counter(dict(G_filtered_bis.degree(weight='w')).values())\n",
    "degree_freq_filtered_bis_weighted = {k:degree_freq_filtered_bis_weighted[k] for k in sorted(degree_freq_filtered_bis_weighted.keys())}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "efe02b2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# clustering coefficient only on the giant component\n",
    "clustering_coeff_filtered_bis = nx.average_clustering(G_filtered_bis, nodes=c[0], weight='w', count_zeros=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4f14ae7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0033415793673618496"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clustering_coeff_filtered_bis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84521e9d",
   "metadata": {},
   "source": [
    "## 6.4  - Compare the 3 degree distributions of the previous networks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a599d72",
   "metadata": {},
   "source": [
    "### 6.4.1 - Unweighted degree distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3fa67de9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 468x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = plt.cm.tab20.colors\n",
    "ig = plt.figure(figsize=(6.5, 3))\n",
    "ax = plt.axes()\n",
    "ax.plot(range(len(degree_freq)), degree_freq, marker='o',\n",
    "        linewidth=0, color=cmap[0], markersize=7, label=r'Original', alpha=1)\n",
    "ax.plot(range(len(degree_freq_filtered)), degree_freq_filtered, marker='o',\n",
    "        linewidth=0, color=cmap[8], markersize=7, label=r'Filtered', alpha=1)\n",
    "ax.plot(range(len(degree_freq_filtered_bis)), degree_freq_filtered_bis, marker='o',\n",
    "        linewidth=0, color=cmap[2], markersize=7, label=r'Filtered twice', alpha=1)\n",
    "ax.set_xlabel(r'$k$', fontsize=axisLabelSize)\n",
    "ax.set_xscale('log')\n",
    "ax.set_ylabel(r'$P(k)$', fontsize=axisLabelSize)\n",
    "ax.set_yscale('log')\n",
    "#ax.set_ylim(0.4,1e7)\n",
    "#ax.set_xlim(0.6,1e5)\n",
    "#ax.set_xticks([10**i for i in range(6)], [10**i for i in range(6)])\n",
    "ax.tick_params(which='major', labelsize=ticksLabelSize, length=7, width=ticksWidth)\n",
    "ax.tick_params(which='minor', length=4, width=ticksWidth)\n",
    "ax.legend(loc='best', ncol=1, fontsize=legendSize, frameon=False)\n",
    "plt.savefig(f'{path_output}degree_distribs_OriginalDataset_vs_FilteredDataset_vs_FilteredDatasetBIS.pdf', format='pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "364fa9ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "degree_freq_dict = {d:v for d,v in enumerate(degree_freq)}\n",
    "x_original,y_original = log_binning(degree_freq_dict,50)\n",
    "degree_freq_filtered_dict = {d:v for d,v in enumerate(degree_freq_filtered)}\n",
    "x_filtered,y_filtered = log_binning(degree_freq_filtered_dict,50)\n",
    "degree_freq_filtered_bis_dict = {d:v for d,v in enumerate(degree_freq_filtered_bis)}\n",
    "x_filtered_bis,y_filtered_bis = log_binning(degree_freq_filtered_bis_dict,50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "348c7ef7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = plt.cm.tab20.colors\n",
    "ig = plt.figure(figsize=(6, 3))\n",
    "ax = plt.axes()\n",
    "ax.plot(x_original, y_original, marker='o', mfc='none', markeredgewidth=1.5,\n",
    "        linewidth=0, color=cmap[0], markersize=7, label=r'Original', alpha=1)\n",
    "ax.plot(x_filtered, y_filtered, marker='s', mfc='none', markeredgewidth=1.5,\n",
    "        linewidth=0, color=cmap[8], markersize=7, label=r'Filtered', alpha=1)\n",
    "ax.plot(x_filtered_bis, y_filtered_bis, marker='d', mfc='none', markeredgewidth=1.5,\n",
    "        linewidth=0, color=cmap[2], markersize=7, label=r'Filtered twice', alpha=1)\n",
    "ax.set_xlabel(r'$k$', fontsize=axisLabelSize)\n",
    "ax.set_xscale('log')\n",
    "ax.set_ylabel(r'$P(k)$', fontsize=axisLabelSize)\n",
    "ax.set_yscale('log')\n",
    "#ax.set_ylim(0.4,1e7)\n",
    "#ax.set_xlim(0.6,1e5)\n",
    "#ax.set_xticks([10**i for i in range(6)], [10**i for i in range(6)])\n",
    "ax.tick_params(which='major', labelsize=ticksLabelSize, length=7, width=ticksWidth)\n",
    "ax.tick_params(which='minor', length=4, width=ticksWidth)\n",
    "ax.legend(loc='best', ncol=1, fontsize=legendSize, frameon=False)\n",
    "plt.savefig(f'{path_output}degree_distribs_LogBinned_OriginalDataset_vs_FilteredDataset_vs_FilteredDatasetBIS.pdf', format='pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f61e5028",
   "metadata": {},
   "source": [
    "### 6.4.2 - Weighted degree distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "48987d54",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_248177/3539005830.py:16: RuntimeWarning: invalid value encountered in true_divide\n",
      "  bin_means_y = (np.histogram(list(counter_dict.keys()),bins,weights=list(counter_dict.values()))[0]/np.histogram(list(counter_dict.keys()),bins)[0])\n",
      "/tmp/ipykernel_248177/3539005830.py:17: RuntimeWarning: invalid value encountered in true_divide\n",
      "  bin_means_x = (np.histogram(list(counter_dict.keys()),bins,weights=list(counter_dict.keys()))[0]/np.histogram(list(counter_dict.keys()),bins)[0])\n"
     ]
    }
   ],
   "source": [
    "#degree_freq_dict = {d:v for d,v in enumerate(degree_freq_weighted)}\n",
    "x_original,y_original = log_binning(degree_freq_weighted,50)\n",
    "#degree_freq_filtered_dict = {d:v for d,v in enumerate(degree_freq_filtered_weighted)}\n",
    "x_filtered,y_filtered = log_binning(degree_freq_filtered_weighted,50)\n",
    "#degree_freq_filtered_bis_dict = {d:v for d,v in enumerate(degree_freq_filtered_bis_weighted)}\n",
    "x_filtered_bis,y_filtered_bis = log_binning(degree_freq_filtered_bis_weighted,50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "45b65c68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 468x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = plt.cm.tab20.colors\n",
    "ig = plt.figure(figsize=(6.5, 3))\n",
    "ax = plt.axes()\n",
    "ax.plot(x_original, y_original, marker='o', mfc='none', markeredgewidth=1.5,\n",
    "        linewidth=0, color=cmap[0], markersize=7, label=r'$\\mathcal{A}_{original}$', alpha=1)\n",
    "ax.plot(x_filtered, y_filtered, marker='s', mfc='none', markeredgewidth=1.5,\n",
    "        linewidth=0, color=cmap[8], markersize=7, label=r'$\\mathcal{A}_{C1}$', alpha=1)\n",
    "ax.plot(x_filtered_bis, y_filtered_bis, marker='d', mfc='none', markeredgewidth=1.5,\n",
    "        linewidth=0, color=cmap[2], markersize=7, label=r'$\\mathcal{A}_{C2}$', alpha=1)\n",
    "ax.set_xlabel(r'$k$', fontsize=axisLabelSize)\n",
    "ax.set_xscale('log')\n",
    "ax.set_ylabel(r'$P(k)$', fontsize=axisLabelSize)\n",
    "ax.set_yscale('log')\n",
    "#ax.set_ylim(0.4,1e7)\n",
    "#ax.set_xlim(0.6,1e5)\n",
    "#ax.set_xticks([10**i for i in range(6)], [10**i for i in range(6)])\n",
    "ax.tick_params(which='major', labelsize=ticksLabelSize, length=7, width=ticksWidth)\n",
    "ax.tick_params(which='minor', length=4, width=ticksWidth)\n",
    "ax.legend(loc='lower left', ncol=1, fontsize=legendSize, frameon=False)\n",
    "plt.savefig(f'{path_output}degree_distribs_WEIGHTED_LogBinned_OriginalDataset_vs_FilteredDataset_vs_FilteredDatasetBIS.pdf', format='pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fb49ec8",
   "metadata": {},
   "source": [
    "## 6.5 - Compare the graph at the different filtering steps with a distance "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0831bb4",
   "metadata": {},
   "source": [
    "### 6.5.1 - Network Portrait Divergence\n",
    "\n",
    "Refs:\n",
    "- https://github.com/bagrow/network-portrait-divergence\n",
    "- Bagrow & Bollt (2019): https://appliednetsci.springeropen.com/articles/10.1007/s41109-019-0156-x\n",
    "- Other methods of network comparison and alignment in Tantardini et al. (2019): https://www.nature.com/articles/s41598-019-53708-y\n",
    "\n",
    "**WARNING: Too much time to execute**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b2808123",
   "metadata": {},
   "outputs": [],
   "source": [
    "path_code = r'/.../network_portrait_divergence_master/'\n",
    "os.chdir(path_code)\n",
    "from portrait_divergence import portrait_divergence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2bf76f57",
   "metadata": {},
   "outputs": [],
   "source": [
    "d_Original_Filtered = portrait_divergence(G_original,G_filtered)\n",
    "d_Original_Filtered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55be7736",
   "metadata": {},
   "outputs": [],
   "source": [
    "d_Original_FilteredBis = portrait_divergence(G_original,G_filtered_bis)\n",
    "d_Original_FilteredBis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0422ecfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "d_Filtered_FilteredBis = portrait_divergence(G_filtered,G_filtered_bis)\n",
    "d_Filtered_FilteredBis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6984da2d",
   "metadata": {},
   "source": [
    "### 6.5.2 - Network Laplacian Spectral Distance (NetLSD)\n",
    "\n",
    "Refs:\n",
    "- Tsitsulin et al. (2018): https://arxiv.org/pdf/1805.10712\n",
    "- Codes on: https://github.com/xgfs/NetLSD\n",
    "\n",
    "The method exploit the properties of the laplacian matrix $\\mathcal{L}$ of a graph $G$. As its eigenvalues reflect the properties of the graph at many scales, the authors apply a diffusion model (heat or wave), inherited from information transfer between the nodes of the graph, in order to estimate how the information is transfered between two nodes and to reflect the different centrality structures of the graph at all scales.\n",
    "The computation is based on the resolution of a heat (or wave) equation, based on $\\mathcal{L}$ and where the nodes have a heat $u_t$ to diffuse throughout the graph that takes specific values at different time $t$ (which has to be dissipated).\n",
    "The goal is to estimate the temporal *heat kernel* (or *wave kernel*) of the graph, ie. a matrix between all the nodes called $H_t$, expressed as an exponentiation of the eigenvalues of $\\mathcal{L}$ and the time of the diffusion process. The author summarize the state of the diffusion process on the graph at a given time with the trace $h_t$ of the associated matrix $H_t$ at time $t$.\n",
    "In this way, we thus get a vector of traces of the heat kernel at different time $t$, called $h(G)=\\{h_t\\}_{t>0}$, which represents the way the diffusion process is done between the different nodes at the different scales.\n",
    "If the signatures of two graphs are similar, it thus mean that their respective topology are also similar, since they have been submitted to a rather common diffusion process between their respective nodes.\n",
    "\n",
    "Pros: \n",
    "- can use a `scipy.sparse` edge list to compute the laplacian of the input graph, which would be used to compute the signature (here, the trace of the solution of the heat equation of the laplacian).\n",
    "- use a technique to interpolate the spectrum of the graph, based on a small number of eigenvalues (mainly the extrem ones) rather than the whole set which require too much time and memory to compute. It restrains artificially the number of eigenvalues, whose set is approximating actually the original laplacian. This interpolation technique is worth for comparing networks which don't have the same number of nodes, by comparing truncated laplacians with the same number of eigenvalues. Scalability exhibited in the paper: 300 eigenvalues computed in 16 minutes for a graph with $10^6$ nodes.\n",
    "\n",
    "Cons:\n",
    "- works only for undirected graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2211064c",
   "metadata": {},
   "outputs": [],
   "source": [
    "path_input = r'/home/sylvain/DOCTORAT/PUBLICATIONS/2_AI_EpistemicIntegration_SocialSegregation/dataset_Zenodo/'\n",
    "path_output = r'/home/sylvain/DOCTORAT/PUBLICATIONS/2_AI_EpistemicIntegration_SocialSegregation/dataset_Zenodo/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "26b5897f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import netlsd\n",
    "from scipy.sparse import csr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ec771bb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sparse_matrix_from_edgeList_DataFrame(edges, label_source, label_target, label_value, directed):\n",
    "    \n",
    "    # build the mapping function of the nodes\n",
    "    map_node = {j:i for i,j in enumerate(set(edges[label_source])|set(edges[label_target]))}\n",
    "    N = len(map_node)\n",
    "    \n",
    "    # build the correspondance matrix with row's and column's vectors\n",
    "    # don't forget to add the symmetric links\n",
    "    if directed:\n",
    "        matrix = np.array([[map_node[i],map_node[j],k] for i,j,k in edges.values])\n",
    "    else:\n",
    "        matrix = np.array([[map_node[i],map_node[j],k] for i,j,k in edges.values]+[[map_node[j],map_node[i],k] for i,j,k in edges.values])\n",
    "    rows, columns, values = matrix[:,0], matrix[:,1], matrix[:,2]\n",
    "\n",
    "    return csr_matrix((values, (rows, columns)), shape=(N,N))\n",
    "\n",
    "def cosine_similarity(v1,v2):\n",
    "    num = sum([i*j for i,j in zip(v1,v2)])\n",
    "    denom = np.linalg.norm(v1)*np.linalg.norm(v2)\n",
    "    return num/denom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8cd52890",
   "metadata": {},
   "outputs": [],
   "source": [
    "adj_original = get_sparse_matrix_from_edgeList_DataFrame(cn_original,'a1','a2','weight',directed=False)\n",
    "adj_filtered = get_sparse_matrix_from_edgeList_DataFrame(collabNet_timeagg,'author1','author2','w',directed=False)\n",
    "adj_filtered_bis = get_sparse_matrix_from_edgeList_DataFrame(sub_collabNet_timeagg,'author1','author2','w',directed=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0d2cffc",
   "metadata": {},
   "outputs": [],
   "source": [
    "descriptor_original = netlsd.heat(adj_original, \n",
    "                                  timescales=np.logspace(-2, 2, 250), \n",
    "                                  eigenvalues=25, # interpolation with 200 values\n",
    "                                  normalization='empty', \n",
    "                                  normalized_laplacian=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed88f1b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "descriptor_filtered = netlsd.heat(adj_filtered, \n",
    "                                  timescales=np.logspace(-2, 2, 250), \n",
    "                                  eigenvalues=25, &\n",
    "                                  normalization='empty', \n",
    "                                  normalized_laplacian=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6580d0bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "descriptor_filtered_bis = netlsd.heat(adj_filtered_bis, \n",
    "                                  timescales=np.logspace(-2, 2, 250), \n",
    "                                  eigenvalues=25, \n",
    "                                  normalization='empty', \n",
    "                                  normalized_laplacian=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "180c3e01",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(len(descriptor_original), len(descriptor_filtered), len(descriptor_filtered_bis))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "251eb5fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "cosine_similarity(descriptor_original,descriptor_filtered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76045b7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "cosine_similarity(descriptor_original,descriptor_filtered_bis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "841c27d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "cosine_similarity(descriptor_filtered,descriptor_filtered_bis)"
   ]
  }
 ],
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