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1-theory.qmd
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1-theory.qmd
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---
title: "Introduction"
author: "Thomas W. Valente"
date: 2024-12-05
date-modified: 2024-12-05
---
## Outline
- Introduction to the network diffusion model and the motivation for
netdiffuseR
- The classic datasets (MI, BF & KFP)
- Simulating diffusion with netdiffuseR
- Data management features
- The FCTC study -- multiple networks both static and dynamic
## Acknowledgements
NetDiffuseR was created with the support of grant R01 CA157577 from the
National Cancer Institute/National Institutes of Health.
It has benefited from input provided by participants of the Center for
Applied Network Analysis at the University of Southern California
## Motivation for NetdifusseR
Build a platform in which:
- network exposure terms can be easily calculated and compared;
- other diffusion network terms can be created (e.g., Moran's I,
infectiousness, susceptibility);
- multiple dynamic and static networks can be analyzed simultaneously;
- Graphical displays of diffusion processes can be made;
- Statistical tests both conventional and new can be conducted;
- Integrate network diffusion models with other analytic approaches;
- and more.
## Diffusion Networks
- Unique set of studies that combine social network data with behavioral
data
- Typically have data from zero or low prevalence to saturation or high
prevalence.
- Multiple time points presents unique opportunities and challenges.
- Data often do not include dis-adoption but the model accounts for it.
- Network data may be static or dynamic.
- Can have one or multiple networks
- Model multiple behaviors simultaneously (e.g., tobacco & alcohol,
awareness & adoption, VHS & Betamax)
- Because we combine networks and behavior
- Can have people in the network with no adoption data
- People with adoption data but not in the network
## Diffusion of Innovations
- Explains how new ideas and practices spread within and between
communities.
- One of the oldest and richest social science theories.
- Many factors influence diffusion
- Spatial
- Economic
- Cultural
- Developmental
- Biological
- Etc.
- One of the most significant is social networks
---
{width="80%"}
## Diffusion Networks
{width="80%"}
## Structural Equivalence is Associated with Influence
{width="80%"}
Burt, R. (1987) **Social contagion and innovation: Cohesion versus structural equivalence**. American Journal of Sociology, 92, 1287-1335.
## My article discovered network thresholds
{width="80%"}
## Graph of Time of Adoption by Network Threshold for One Korean Family Planning Community
{width="80%"}
## Indirect Exposures Matter
{width="80%"}
## Alter Attributes May Affect Influence
{width="80%"}
## Centrality Weighted Exposures
{width="80%"}
---
{width="80%"}
---
- Node A: Threshold=3/5, Susceptibility=1, Susc_Normed=1/3
- Node E: Impact 3/5, Infectiousness=1, Infect_Normed=1/3
{width="80%"}
## Other Features: Extending Exposure
- Thresholds
- Indirect exposures
- Attribute weighted
- Network weighted (i.e., centrality)
- Infectiousness
- Susceptibility
- Ego-alter table
- Graphing!
## 1995 Reported on the 3 Classic Studies
{width="40%" fig-align="center"}
- 3 classic diffusion network datasets
- MI: Medical Innovation
- BF: Brazilian Farmers
- KFP: Korean Family Planning
## Diffusion Curves for Classic Studies
{width="80%"}
## 6 Diffusion Network Studies 3 Early Studies Lost
- Rogers' dissertation on 2-4D Weed Spray
- Rogers' work in Colombia
- Becker's study of public health officers in New York, Ohio, and
Michigan (compared 2 innovations)
## 3 Classic Diffusion Network Studies
| | Medical Innovation | Korean Family Planning | Brazilian Farmers |
|--------------------|-----------------------|-------------------|--|
| Country | USA | Korean | Brazil |
| \# Respondents | 125 Doctors | 1,047 Women | 692 Farmers |
| \# Communities | 4 | 25 | 11 |
| Innovation | Tetracycline | Family Planning | Hybrid Corn Seed |
| Time for Diffusion | 18 Months | 11 Years | 20 Years |
| Year Data Collected| 1955 | 1973 | 1966 |
| Ave. Time to 50% | 6 | 7 | 16 |
| Highest Saturation | 89% | 83% | 98% |
| Lowest Saturation | 81% | 44% | 29% |
| Citation | Coleman et al (1996) | Rogers & Kincaid (1981) | Rogers et al (1970) |
## Reconfigure to Event History Data
{width="80%"}
## Medical Innovation (Coleman, Katz, & Menzel, 1966),
Data collected from physicians in the 4 cities in Illinois in 1955
(Galesburg, Bloomington, Quincy, & Peoria). 125 MDs interviewed about
the medical practice characteristics:
- 3 network questions: advice, discussion, and friends.
- Adoption data collected by analyzing the prescription records of local
pharmacies on the first 3 days of each month.
## Brazilian Farmers (Rogers, Ascroft, & Röling, 1970)
As part of the USAID funded 3-country study, farmers in selected
villages in India, Nigeria, and Brazil were interviewed about their
farming practices, use of media, and other personal characteristics.
Networks were measured by asking:
- their three best friends, (2) the three most influential people in
their community, (3) the three most influential people regarding various
farm innovations, and (4) the best person to organize a cooperative
project.
- Adoption data were collected by asking farmers when they first planted
hybrid seed corn
## Korean Family Planning (Rogers & Kincaid, 1981)
Scholars at Seoul National University\'s School of Public Health
collected data on the adoption of family planning methods among all
married women of child-bearing age in 25 Korean villages in 1973
(N=1,047). Networks Network data were obtained by asking respondents to
name five people from whom
- they sought advice about (1) family planning, (2) general information,
(3) abortion, (4) health, (5) the purchase of consumer goods, and (6)
children\'s education.
- Adoption data were obtained by asking respondents to state the year
they first used a modern family planning method.
---
{width="90%"}
---
{width="90%"}
---
{width="90%"}
## Exposure results
| Term | MI | KFP | BF |
|------|----|-----|----|
| Detail Agents | 1.27 | | |
| Science Orientation | 0.60\*\* | | |
| Journals Subs. | 1.63\* | | |
| \# Sons | | 1.43\*\* | |
| Media Camp. Exp. | | 1.10\*\* | |
| Income | | | 1.18\*\* |
| Visits to City | | | 1.00 |
| Out Degree | 0.96 | 1.05 | 0.98 |
| In Degree | 1.04 | 1.06\*\* | 1.02\* |
| Exposure (Cohesion) | 0.94 | 1.16 | 2.16\*\* |
## Simulating diffusion
- Seeds:
- Type (central, bridging, marginal, random, etc.)
- Size (e.g., 5%, 10%, 15%)
- Network structure:
- Random
- Small world
- Empirical
- Scale-free (centralized)
- Attribute-based (e.g., homophily)
- Threshold distribution:
- Uniform
- Normal (varying variance)
- Skewed
- Beta
- Mechanism:
- Cohesion
- Structural equivalence
- Indirect ties
- Attribute-based (e.g., homophilous more persuasive)
- Adoption behavior:
- No-disadoption
- Disadoption
- Conflicting behaviors (perhaps coded as -1,0, 1)
- Incorporate uncertainty
## Reading Data Challenges
- Merging attribute and network data
- Nominated but no attribute data
- Attribute data no network data
- Data file formats
- Single flat file
- 2 files: edgelist and attribute file (flat)
- Cohort studies: 1 file separate waves of data
## Formats
- Survey Data (static & dynamic)
- Edge-list and Attribute data
- Panel Data
- STATNET, Igraph, ....
## Input Types: Single Flat File
- Flat File with IDs, Nodelist, and Time of Adoption
- Typically a retrospective study
- Examples, Medical Innovation, Korean Family Planning; Brazilian Farmers
{width="40%"}
## Input Types: Double Files
- Files will have mostly matching IDs
- One or both files may contain time information
- Edgelist may also, potentially, contain values or spell information
{width="80%"}
## Input Types: Classic Cohort/Longitudinal
- IDs and Nodelist Network data
- Behavior is typically binary (e.g., ever smoked) or potentially valued (e.g., # of cigarettes
- Examples SNS
{width="50%"}