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Inferential Network Analysis

Philip Leifeld

University of Glasgow

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.

Twitter  @PhilipLeifeld

Course Dates and Times

Monday 29 February to Friday 4 March 2016
Generally classes are either 09:00-12:30 or 14:00-17:30
15 hours over 5 days

Prerequisite Knowledge

This is an advanced course on network analysis. The course assumes existing knowledge of basic social science research methods at least through generalized linear models (logit, the linear model, etc...) as well as basic knowledge of network analysis. Participants should know the basic anatomy of networks as well as the descriptive tools of network analysis (e.g. measures of centrality, plotting and visualization, etc...). Lastly, all techniques will be demonstrated using the R statistical language. While this is not a course *about* software, basic familiarity with R will be quite helpful for students as 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. It would be helpful if participants could install R and the packages statnet and xergm before the course starts.

Short Outline

This course will cover topics in the advanced modeling of social and political networks. The primary goal is to move beyond descriptive analysis of networks and develop inferential models; primarily the exponential random graph model (ERGM), but alternative techniques such as the stochastic actor-based model (i.e. SIENA), the quadratic assignment procedure, latent space models, and the temporal network autocorrelation model will be considered as well.

Long Course Outline

This 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 for the course will be the development of 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 that is inherent to network data.

In order to accomplish the above, we will consider two basic approaches to modelling networks. The approach we will spend the most time on 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, and limitations. We will then extend our knowledge of this approach to longitudinal networks by considering both the Temporal ERGM and the stochastic actor oriented model (SAOM, more commonly known as SIENA), which are closely related.

We will close by considering alternative approaches to modelling networks, including the latent space network model, in which the network dependencies are projected into the error term rather than explicitly modelled, the quadratic assignment procedure, and the temporal network autocorrelation model, a model for the behaviour of nodes in a network. For each topic we cover, we will also consider how to perform such analyses in R using several example datasets.

Day Topic Details
1 Introduction and why we need network-specific models (and not regressions). Lecture
2 Introduction to the ERGM, form, specification, estimation, and interpretation. Lecture with some lab
3 The ERGM, form, specification, estimation, and interpretation (continued). Some lecture with substantial lab
4 Longitudinal network models: TERGM and SIENA. Mostly lecture, some lab
5 Latent Space Models, QAP, and autocorrelation models. Mostly lecture, some lab
Day Readings
1 - Lusher, Dean, Johan Koskinen and Garry Robins. 2013. Exponential Random Graph Models for Social Networks. New York, NY: Cambridge University Press. Chapters 2-5. - Cranmer, Skyler J., Philip Leifeld, Scott McClurg, and Meredith Rolfe (2015): Navigating the Range of Statistical Tools for Inferential Network Analysis. Working Paper.
2 - Cranmer, Skyler J. 2011. Inferential Network Analysis with Exponential Random Graph Models. Political Analysis 19: 66-86. - Leifeld, Philip and Volker Schneider 2012. Information Exchange in Policy Networks. American Journal of Political Science 53(3): 731-744.
3 - Hunter, David R., Mark S. Handcock, Carter T. Butts, Steven M. Goodreau and Martina Morris. 2008. "ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks." Journal of Statistical Software 24(3):1-29. - Goodreau, Steven M., Mark S. Handcock, David R. Hunter, Carter T. Butts and Martina Morris. 2008. "A statnet tutorial." Journal of Statistical Software 24(9): 1-26
4 - Leifeld, Philip, Skyler J. Cranmer and Bruce A. Desmarais (2015): Temporal Exponential Random Graph Models with xergm: Estimation and Bootstrap Confidence Intervals. Working Paper. - 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. Working Paper.
5 - Leifeld, Philip and Skyler J. Cranmer (2015): The Temporal Network Autocorrelation Model. Working Paper.

Software Requirements

All software we use will be free: R and several of its packages (e.g. ergm, statnet, xergm).

Hardware Requirements

Students to bring their own laptops.


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.

Butts, C. T. (2008a). A relational event framework for social action. Sociological Methodology, 38(1):155–200.

Butts, C. T. (2008b). network: A package for managing relational data in R. Journal of Statistical Software, 24(2):1–36.

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.

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.

Handcock, M. S., Hunter, D. R., Butts, C. T., Goodreau, S. M., and Morris, M. (2008). statnet: Software tools for the representation, visualization, analysis and simulation of network data. Journal of Statistical Software, 24(1):1–11.

Hanneke, Steve, Wenjie Fu and Eric P. Xing. 2010. "Discrete Temporal Models of Social Networks." Electronic Journal of Statistics 4:585-605.

Hoff, Peter D., Adrian E. Raftery and Mark S. Handcock. 2002. "Latent Space Approaches to Social Network Analysis." Journal of the American Statistical Association 97(460):pp. 1090-1098.

Ingold, Karin and Philip Leifeld (forthcoming): Structural and Institutional Determinants of Influence Reputation: A Comparison of Collaborative and Adversarial Policy Networks in Decision Making and Implementation. Journal of Public Administration Research and Theory.

Krackhardt, David. 1988. Predicting with networks: Nonparametric multiple regression analysis of dyadic data. Social Networks 10(4):359-381.

Krivitsky, Pavel N. and Mark S. Handcock. 2008. "Fitting Latent Cluster Models for Networks with latentnet." Journal of Statistical Software 24(5):1-23.

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.

Morris, M., Handcock, M. S., and Hunter, D. R. (2008). Specification of exponential- family random graph models: Terms and computational aspects. Journal of Statistical Software, 24(4):1–24.

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.

Snijders, Tom A.B., Gerhard G. van de Bunt and Christian E.G. Steglich. 2010. "Introduction to stochastic actor-based models for network dynamics." Social Networks 32(1):44 – 60.

Recommended Courses to Cover Before this One

<p>Winter School WD102 Introduction to Applied Social Network Analysis (NOT STRICTLY REQUIRED)</p>

Additional Information


This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc). Registered participants will be informed in due time.

Note from the Academic Convenors

By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, contact the instructor before registering.