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Monday 25 February – Friday 1 March, 09:00–12:30
15 hours over 5 days
See under 'Prerequisite Knowledge'
Tasks for ECTS Credits
2 credits (pass/fail grade) Attend at least 90% of course hours, participate fully in in-class activities, and carry out the necessary reading and/or other work prior to, and after, classes.
3 credits (to be graded) As above, plus complete daily assignments.
4 credits (to be graded) As above, plus complete a seminar paper with a grade higher than F.
Until March 2019, Philip Leifeld is Professor of Research Methods at the University of Glasgow in the School of Social and Political Sciences. From April 2019, he takes up a post as Professor of Comparative Politics in the Department of Government at the University of Essex.
Philip's research interests are social and political networks, quantitative methods, policy debates, and the study of policy processes.
His work has appeared in a number of journals, such as the American Journal of Political Science and the Journal of Statistical Software.
@PhilipLeifeldThis course revolves around the idea of creating probabilistic statistical models of networks. This is a big departure from the descriptive analysis of networks (e.g., measuring the centrality of a node) and also a fairly big departure from the statistical modelling of non-network data with the regression framework.
Our goal is to develop statistical models that can accomplish the same general objectives as regression models (fitting parameters to data with probabilistic models), while accounting for the substantial endogenous complexity inherent to network data.
To accomplish this, we will consider two basic approaches to modelling networks.
The approach on which we will spend the most time involves explicitly modelling the network dependencies present in the data. Starting cross-sectionally, we will introduce the exponential random graph model (ERGM) and consider it in some detail – including specification, estimation, fit checking, diagnosing problems, limitations, and post-estimation analysis and interpretation. We will then extend our knowledge of this approach to longitudinal, repeatedly observed networks by considering the Temporal ERGM and the stochastic actor-oriented model (SAOM, more commonly known as SIENA), which are closely related.
We close by considering alternative approaches to modelling networks, including the latent space network model and the quadratic assignment procedure, in which network dependencies are projected into the error term rather than explicitly modelled.
We will also discuss two additional models for temporal data: the temporal network autocorrelation model (TNAM), which is a model for the behaviour of nodes in a network, and the relational event model (REM), which can model a temporally more fine-grained series of network ties.
For each topic we cover, we will also consider how to perform such analyses in R using several example datasets.
If you want, you can present your own problems in class, and benefit from constructive feedback from the group.
What you will learn
This course will enable you to think about your own network data from a statistical and theoretical point of view. You will learn how to translate their theoretical questions into statistical models and how to answer these questions using empirical data and estimation.
This is an advanced course, which assumes existing knowledge of basic social science research methods, at least through generalised linear models (logit, the linear model, etc...) as well as basic knowledge of network analysis.
You should know the basic anatomy of networks as well as the descriptive tools of network analysis (e.g., measures of centrality, plotting and visualisation, etc...).
All techniques will be demonstrated using the R statistical language.
While this is not a course *about* software, basic familiarity with R is helpful, because I will not go into detail about how to load/manage data or use R's more basic functions. That said, a high level of R (e.g., programming competency) is not necessary.
Please install R and the packages statnet and xergm before the course.
| Day | Topic | Details |
|---|---|---|
| 1 | Introduction and why we need network-specific models (and not regressions). |
75% lecture on network modelling; |
| 2 | Introduction to the ERGM, form, specification, estimation, and interpretation. |
75% lecture on ERGM; |
| 3 | The ERGM, form, specification, estimation, and interpretation (continued). |
75% lecture on ERGM; |
| 4 | Longitudinal network models: TERGM, TNAM, and SAOM. |
75% lecture on TERGM, TNAM, and SAOM; |
| 5 | Latent Space Models, QAP, temporal network autocorrelation models, and relational event models. |
75% lecture on LSM, QAP, and REM; |
| Day | Readings |
|---|---|
| Monday |
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| Tuesday |
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| Wednesday |
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| Thursday |
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| Friday |
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All software we use is free: R and several of its packages (e.g. ergm, statnet, xergm).
Bring your own laptop (Windows, Linux, MacOS).
Berardo, Ramiro and John T. Scholz. 2010
Self-Organizing Policy Networks: Risk, Partner Selection, and Cooperation in Estuaries
American Journal of Political Science 54(3):632-649
Cranmer, Skyler J., Bruce A. Desmarais and Elizabeth Menninga. 2012
Complex Dependencies in the Alliance Network
Conflict Management and Peace Science 29(3): 279–313
Dekker, D., Krackhardt, D., and Snijders, T. A. B. 2007
Sensitivity of MRQAP tests to collinearity and autocorrelation conditions
Psychometrika, 72(4):563– 581
Desmarais, Bruce A. and Skyler J. Cranmer. 2012
Micro-Level Interpretation of Exponential Random Graph Models with Application to Estuary Networks
Policy Studies Journal 40(3): 402– 434
Desmarais, Bruce A. and Skyler J. Cranmer. 2012
Statistical Mechanics of Networks: Estimation and Uncertainty
Physica A 391(4):1865– 1876
Desmarais, Bruce A. and Skyler J. Cranmer. 2017
Statistical Inference in Political Networks Research
In: Oxford Handbook of Political Networks, edited by Jennifer N. Victor, Alexander H. Montgomery and Mark Lubell
Goodreau, Steven M., James A. Kitts and Martina Morris. 2009
Birds of a feather, or friend of a friend? Using exponential random graph models to investigate adolescent social networks
Demography 46(1):103– 125
Hanneke, Steve, Wenjie Fu and Eric P. Xing. 2010
Discrete Temporal Models of Social Networks
Electronic Journal of Statistics 4:585– 605
Krivitsky, Pavel N. and Mark S. Handcock. 2008
Fitting Latent Cluster Models for Networks with latentnet
Journal of Statistical Software 24(5):1-23
Leifeld, Philip and Skyler J. Cranmer. 2015
A Theoretical and Empirical Comparison of the Temporal Exponential Random Graph Model and the Stochastic Actor-Oriented Model
Leenders, R. T. A. J. 2002
Modeling social influence through network autocorrelation: Constructing the weight matrix
Social Networks, 24(1):21–47
Lerner, J., Bussmann, M., Snijders, T. A. B., and Brandes, U. 2013
Modeling frequency and type of interaction in event networks
Corvinus Journal of Sociology and Social Policy, 4:3–32
Lusher, Dean, Johan Koskinen and Garry Robins. 2013
Exponential Random Graph Models for Social Networks
New York, NY: Cambridge University Press
Robins, G., Pattison, P., Kalish, Y., and Lusher, D. 2007
An introduction to exponential random graph (p*) models for social networks
Social Networks, 29(2):173–191
Schaefer, David R. and Christopher Steven Marcum. 2018
Modeling Network Dynamics
In: Oxford Handbook of Social Network Analysis, edited by James Moody and Ryan Light
Winter School
Introduction to Applied Social Network Analysis
Introduction to R
Linear Regression with R/Stata: Estimation, Interpretation and Presentation
Interpreting Binary Logistic Regression Models
Summer School
R Basics