Social Networks: Statistical Approaches - Online Course
A 3-Day Livestream Seminar Taught by
John Skvoretz10:00am-12:30pm (convert to your local time)
1:30pm-3:30pm
The study of social networks focuses on relationships among the units of some population, and on how the structure of these ties affects outcomes experienced by both the units and the population. Often the units are persons or individuals, but they may be families, households, corporations, or nation states.
Social network analysis is a set of methods for describing, quantifying, and analyzing the properties of social networks. This seminar is a survey of statistical methods for analyzing social network data. It will focus on testing hypotheses about network structure and building models to account for regularities in observed social networks of research interest to the participants.
Starting February 8, this seminar will be presented as a 3-day synchronous, livestream workshop via Zoom. Each day will feature two lecture sessions with hands-on exercises, separated by a 1-hour break. Live attendance is recommended for the best experience. But if you can’t join in real time, recordings will be available within 24 hours and can be accessed for four weeks after the seminar.
Closed captioning is available for all live and recorded sessions. Live captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
ECTS Equivalent Points: 1
More details about the course content
The course begins with models for the local structure of dyads and triads, and next moves to models based on the assumption of dyadic independence. We will then consider models that permit structured forms of dependence between dyads.
Topics include statistical models for local structure (dyads and triads) and graph-level indices, biased net models for complete networks and for aggregated tie count data, exponential random graph models, and stochastic actor-oriented models. Each day will be divided into a presentation of methods and a lab using those methods. This workshop assumes that you have already taken a first course in network analysis, or have acquired equivalent knowledge through self study.
The course begins with models for the local structure of dyads and triads, and next moves to models based on the assumption of dyadic independence. We will then consider models that permit structured forms of dependence between dyads.
Topics include statistical models for local structure (dyads and triads) and graph-level indices, biased net models for complete networks and for aggregated tie count data, exponential random graph models, and stochastic actor-oriented models. Each day will be divided into a presentation of methods and a lab using those methods. This workshop assumes that you have already taken a first course in network analysis, or have acquired equivalent knowledge through self study.
Computing
All software uses the R environment as implemented in RStudio or directly in the standard console interface. Analysis relies on functions and packages developed for that environment including custom functions developed by the instructor and the packages: network, sna, statnet, and RSiena. Basic familiarity with R is highly desirable, but even novice R users should be able to follow the presentation and do the exercises.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent online resources for learning the basics. Here are our recommendations.
All software uses the R environment as implemented in RStudio or directly in the standard console interface. Analysis relies on functions and packages developed for that environment including custom functions developed by the instructor and the packages: network, sna, statnet, and RSiena. Basic familiarity with R is highly desirable, but even novice R users should be able to follow the presentation and do the exercises.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent online resources for learning the basics. Here are our recommendations.
Who should register?
This workshop does assume that you have already taken a first course in network analysis (or have acquired equivalent knowledge through self study). You should be familiar with such network concepts as density, reciprocity, centrality and centralization, and clustering coefficients.
You should also be familiar with statistical concepts at the first-year undergraduate level, including such topics as basic probability distributions, hypothesis testing, t-tests, chi-square tests, OLS regression, and logistic regression.
This workshop does assume that you have already taken a first course in network analysis (or have acquired equivalent knowledge through self study). You should be familiar with such network concepts as density, reciprocity, centrality and centralization, and clustering coefficients.
You should also be familiar with statistical concepts at the first-year undergraduate level, including such topics as basic probability distributions, hypothesis testing, t-tests, chi-square tests, OLS regression, and logistic regression.
Seminar outline
Day 1: Regularities in social networks expected from theory and intuition; testing for effects in dyads, triads, and complete networks; biased net models for realized ties
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- Insights from Gouldner, Granovetter, Feld, Blau, Smalls, Simmel, Burt
- Reciprocity
- Multiplexity
- Exchange
- Transitivity and closure
- Other regularities (density, centralization, clustering, homophily, path lengths)
- Attraction vs repulsion as drivers of intragroup association
Day 2: Exponential random graph models, their specification and estimation with statnet
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- ERGM precursors
- General forms of ERGMs
- Packages to estimate ERGMs
- Classes of ERGMs
- Estimation issues and avoiding degeneracy
Day 3: ERGMS for two-mode, valued and dynamic networks; stochastic actor-oriented models and RSiena
-
- Two mode networks
- Valued or weighted graphs
- Dynamic networks
- SAOM modeling framework
- Specifying and estimating models with RSiena
- Issues of convergence and fit
Day 1: Regularities in social networks expected from theory and intuition; testing for effects in dyads, triads, and complete networks; biased net models for realized ties
-
- Insights from Gouldner, Granovetter, Feld, Blau, Smalls, Simmel, Burt
- Reciprocity
- Multiplexity
- Exchange
- Transitivity and closure
- Other regularities (density, centralization, clustering, homophily, path lengths)
- Attraction vs repulsion as drivers of intragroup association
Day 2: Exponential random graph models, their specification and estimation with statnet
-
- ERGM precursors
- General forms of ERGMs
- Packages to estimate ERGMs
- Classes of ERGMs
- Estimation issues and avoiding degeneracy
Day 3: ERGMS for two-mode, valued and dynamic networks; stochastic actor-oriented models and RSiena
-
- Two mode networks
- Valued or weighted graphs
- Dynamic networks
- SAOM modeling framework
- Specifying and estimating models with RSiena
- Issues of convergence and fit
Payment information
The fee of $995 USD includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $995 USD includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.