Social Networks: Statistical Approaches - Online Course
A 4-Week On-Demand Seminar Taught by
John SkvoretzMonday, September, 8 —
Monday, October 6, 2025
Each Monday you will receive an email with instructions for the following week.
All course materials are available 24 hours a day. Materials will be accessible for an additional 2 weeks after the official close on October 6.
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.
The course takes place online in a series of four weekly installments of videos, readings, and exercises, and requires about 6-8 hours/week. You may participate at your own convenience; there are no set times when you are required to be online.
This four-week course can be accessed with any recent web browser on almost any platform, including iPhone, iPad, and Android devices. It consists of several modules, which contain videos of the 3-day livestream version of the course in its entirety. There are also weekly exercises that ask you to apply what you’ve learned.
There is also an online discussion forum where you can post questions or comments about any aspect of the course. All questions will be promptly answered by Dr. Skvoretz.
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. Topics will be divided into a presentation of methods and a lab using those methods.
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. Topics will be divided into a presentation of methods and a lab using those methods.
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 on-line 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 on-line 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
- 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
- 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
- ERGMS for two-mode, valued and dynamic networks; stochastic actor-oriented models and RSiena
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- Two mode networks
- Valued or weighted graphs
- Dynamic networks
- SAOM modeling framework
- Specifying and estimating models with RSiena
- Issues of convergence and fit
- 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
-
- 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
-
- 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
-
Registration instructions
The fee of $695 (USD) includes all course materials. All major credit cards are accepted.
This course is hosted on a platform called DigitalChalk. To register, you’ll need to go to statisticalhorizons.digitalchalk.com and click on Create Account. Then you will enter your name and email address, and create a password. Be sure to save your password because you will need it to logon to the course itself.
When you have created your account, you’ll be taken to your new home page. Click on the Register Now button (or click the Catalog icon on the left-hand column), and you’ll see “Social Networks: Statistical Approaches” as one of the available courses. At the bottom of the box for that course, click the green button Add to Cart. Next click the green button at the top that says Checkout. You will then be prompted for your credit card information.
When you have finished the payment process, you will be taken back to your home page. Click on Dashboard to see Social Networks: Statistical Approaches. When the course begins on September 8, you can click the play button to get started.
The fee of $695 (USD) includes all course materials. All major credit cards are accepted.
This course is hosted on a platform called DigitalChalk. To register, you’ll need to go to statisticalhorizons.digitalchalk.com and click on Create Account. Then you will enter your name and email address, and create a password. Be sure to save your password because you will need it to logon to the course itself.
When you have created your account, you’ll be taken to your new home page. Click on the Register Now button (or click the Catalog icon on the left-hand column), and you’ll see “Social Networks: Statistical Approaches” as one of the available courses. At the bottom of the box for that course, click the green button Add to Cart. Next click the green button at the top that says Checkout. You will then be prompted for your credit card information.
When you have finished the payment process, you will be taken back to your home page. Click on Dashboard to see Social Networks: Statistical Approaches. When the course begins on September 8, you can click the play button to get started.