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Member rate £492.50
Non-Member rate £985.00
Save £45 Loyalty discount applied automatically*
Save 5% on each additional course booked
*If you attended our Methods School in the last calendar year, you qualify for £45 off your course fee.
Monday 5 to Friday 9 March 2018
09:00-12:30
15 hours over 5 days
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. Participants should install R and the packages statnet and xergm before the course starts.
Tasks for ECTS Credits
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 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, limitations, and post-estimation analysis and interpretation. We will then extend our knowledge of this approach to longitudinal, repeatedly observed 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 and the quadratic assignment procedure, in which the 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.
The course aims to enable students to think about their own network data from a statistical and theoretical point of view. Students will learn how to translate their theoretical questions into statistical models and how to answer these questions using empirical data and estimation. Participants who wish to do so will be given the opportunity to present their own problems in the classroom and benefit from a group discussion.
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. Participants should install R and the packages statnet and xergm before the course starts.
| Day | Topic | Details |
|---|---|---|
| 1 | Introduction and why we need network-specific models (and not regressions). |
75% lecture on network modelling; 25% R lab on data preparation for network analysis. |
| 2 | Introduction to the ERGM, form, specification, estimation, and interpretation. |
75% lecture on ERGM; 25% R lab on ERGM specification, estimation, and interpretation. |
| 3 | The ERGM, form, specification, estimation, and interpretation (continued). |
75% lecture on ERGM; 25% R lab on ERGM specification, estimation, and interpretation. |
| 4 | Longitudinal network models: TERGM, TNAM, and SAOM. |
75% lecture on TERGM, TNAM, and SAOM; 25% R lab on implementation in R. |
| 5 | Latent Space Models, QAP, temporal network autocorrelation models, and relational event models. |
75% lecture on LSM, QAP, and REM. 25% R lab on implementation in R. |
| Day | Readings |
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All software we use will be free: R and several of its packages (e.g. ergm, statnet, xergm).
Students to bring their own laptops (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. https://arxiv.org/abs/1506.06696.
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