tutorials/XGI in 1 minute.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGI in 1 minute 🚀\n",
    "\n",
    "Hello! 👋\n",
    "\n",
    "Let's dive right in and import our XGI library. We just need to execute the following code snippet:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgi"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we're ready to start using XGI!\n",
    "\n",
    "## Creating a Hypergraph\n",
    "\n",
    "You have the flexibility to create a hypergraph from various data structures. One of the simplest ways is by using a list of hyperedges. Check out this code snippet:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "hyperedges = [[1, 2, 3], [3, 4], [4, 5, 6, 7]]\n",
    "H = xgi.Hypergraph(hyperedges)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding Nodes and Hyperedges\n",
    "\n",
    "Once you have a hypergraph, you can start adding single nodes and hyperedges using these convenient functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "H.add_node(8)\n",
    "H.add_edge([7, 8])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to add multiple nodes or hyperedges at once, XGI has got you covered:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "H.add_nodes_from([9, 10])\n",
    "H.add_edges_from([[1, 10], [8, 9, 10]])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Your Hypergraph\n",
    "\n",
    "Visualization is key to understanding complex data structures. With XGI, you can effortlessly plot your hypergraph using the default drawing setup:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgi.draw(H)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " For more options, explore the [focus tutorial on plotting](https://github.com/xgi-org/xgi/blob/main/tutorials/Tutorial%205%20-%20Plotting.ipynb) or consult the [documentation](https://xgi.readthedocs.io/en/stable/api/drawing.html)!"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring Hypergraph Details\n",
    "\n",
    "XGI provides handy functions to gain insights into your hypergraph. For a quick summary, simply print the hypergraph:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unnamed Hypergraph with 10 nodes and 6 hyperedges\n"
     ]
    }
   ],
   "source": [
    "print(H)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also access the number of nodes and hyperedges easily:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "print(H.num_nodes)\n",
    "print(H.num_edges)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And if you're curious about all the hyperedges in your hypergraph, you can retrieve them as a list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{1, 2, 3}, {3, 4}, {4, 5, 6, 7}, {7, 8}, {1, 10}, {8, 9, 10}]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H.edges.members()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wrapping Up\n",
    "\n",
    "Time flies! Don't forget to check out tutorials [here](https://github.com/xgi-org/xgi/tree/main/tutorials)!. And guess what? XGI doesn't stop at hypergraphs — it can handle [simplicial complexes](https://xgi.readthedocs.io/en/stable/api/core/xgi.core.simplicialcomplex.SimplicialComplex.html) and [directed hypergraphs](https://xgi.readthedocs.io/en/stable/api/core/xgi.core.dihypergraph.DiHypergraph.html) too!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}