Please note: This course will be taught online only. In person study is not available for this course.


Michael T. Heaney is a Lecturer in Politics and Research Methods at the University of Glasgow in Scotland.  He is a political scientist who studies how social networks, social movements, interest groups, and political parties shape organizational processes and policy outcomes.  With Fabio Rojas, he is author of Party in the Street: The Antiwar Movement and the Democratic Party after 9/11 (Cambridge University Press, 2015).  With Melody Shemtov and Marco Roldán, he is creator, producer, and writer of a documentary film, The Activists: War, Peace, and Politics in the Streets (Bullfrog Films, 2017).  His articles appear in journals such as the American Political Science Review, the American Journal of Sociology, the Journal of PoliticsPerspectives on PoliticsSocial NetworksScience AdvancesAmerican Politics Research, and the Journal of Health Politics, Policy and Law.   He has received research grants from sources such as the Russell Sage Foundation, the National Science Foundation (USA), and the National Institute for Civil Discourse (USA).

Michael received a Ph.D. in Political Science and Public Policy from the University of Chicago.  He has been a faculty member at the University of Michigan and the University of Florida, a research fellow at the University of Glasgow, a postdoctoral fellow at Yale University, and a Congressional Fellow for the U.S. House Committee on Energy and Commerce.

Course description

This advanced course in social network analysis builds on prior training that students have had in this area, including some prior attention to both descriptive and inferential network analysis.  Topics include missing data, relational event models, community detection, and latent space models.  All students are expected to work (with the guidance and assistance of the instructor) on an empirical research project related to networks, either based on data collected by another scholar (e.g., replication materials from an article or book) or which the student collects themself.  The course culminates with 15-minute, conference-style presentations of research by each student on the final day.


— Knowledge of the fundamentals of probability and statistics;

— Knowledge of ordinary least squares, probit, and logit;

— Introductory training on network analysis including some exposure to both descriptive and inferential network analysis; and

— Some knowledge of working with R.

Learning outcomes

— Students will deepen their prior knowledge of social network analysis.

— Students will learn advanced network-analysis techniques and approaches that are not typically covered in introductory network analysis courses.

— Students will strengthen their ability to use R for network analysis.

— Students will strengthen their ability to conduct and present research using social network analysis.

Required reading


Background Knowledge


Linear Regression = Moderate


OLS = Moderate

Maximum Likelihood = Moderate


R = Moderate

Course Outline

Day 1 — Review of Descriptive Network Analysis

Day 2 — Review of Inferential Network Analysis

Day 3 — Missing Data

Day 4 — Relational Event Models

Day 5 — Community Detection

Day 6 — Discourse Network Analysis

Day 7 — Latent Space Models 1

Day 8 — Latent Space Models 2

Day 9 — Project-based work

Day 10 — Presentations / Exam