ECPR Winter School
University of Bamberg, Bamberg
2 - 9 March 2018




WD208 - Inferential Network Analysis

Instructor Details

Instructor Photo

Philip Leifeld

Institution:
University of Glasgow

Instructor Bio

Philip Leifeld is a senior lecturer (associate professor) in research methods in the School of Social and Political Sciences, University of Glasgow.

His research focuses on social and political networks, quantitative methods, policy debates, and the study of policy processes.

Philip's work has been published in the American Journal of Political Science, the Journal of Statistical Software, and elsewhere.


Course Dates and Times

Monday 5 to Friday 9 March 2018
09:00-12:30
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. Participants should install R and the packages statnet and xergm before the course starts.

 

Short Outline

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

  • Participants attending the course: 2 credits (pass/fail grade) The workload for the calculation of ECTS credits is based on the assumption that students attend classes and carry out the necessary reading and/or other work prior to, and after, classes.
  • Participants attending the course and completing one task (see below): 3 credits (to be graded)
  • Participants attending the course, and completing two tasks (see below): 4 credits (to be graded)
  1. 4 credits: complete daily assignments; seminar paper with grade better than F; regular attendance.
  2. 3 credits: complete daily assignments; regular attendance.
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, 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.

Day-to-Day Schedule

Day 
Topic 
Details 
1Introduction and why we need network-specific models (and not regressions).

75% lecture on network modelling; 25% R lab on data preparation for network analysis.

2Introduction to the ERGM, form, specification, estimation, and interpretation.

75% lecture on ERGM; 25% R lab on ERGM specification, estimation, and interpretation.

3The ERGM, form, specification, estimation, and interpretation (continued).

75% lecture on ERGM; 25% R lab on ERGM specification, estimation, and interpretation.

4Longitudinal network models: TERGM, TNAM, and SAOM.

75% lecture on TERGM, TNAM, and SAOM; 25% R lab on implementation in R.

5Latent 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-to-Day Reading List

Day 
Readings 
Monday
  • 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 D. McClurg and Meredith Rolfe. 2017. Navigating the Range of Statistical Tools for Inferential Network Analysis. American Journal of Political Science. 61(1): 237-251.
  • Butts, C. T. (2008). network: A Package for Managing Relational Data in R. Journal of Statistical Software, 24(2):1–36.
  • 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.
Tuesday
  • 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.
  • Cranmer, Skyler J. 2011. Inferential Network Analysis with Exponential Random Graph Models. Political Analysis 19: 66-86.
  • 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.
  • Morris, Martina, Handcock, Mark S. and Hunter, David R. 2008. Specification of Exponential-family Random Graph Models: Terms and Computational Aspects. Journal of Statistical Software 24(4):1-24.
Wednesday
  • Leifeld, Philip and Volker Schneider. 2012. Information Exchange in Policy Networks. American Journal of Political Science 53(3): 731-744.
  • Leifeld, Philip and Dana R. Fisher. 2017. Membership Nominations in International Scientific Assessments. Nature Climate Change.
  • Heaney, Michael T. and Philip Leifeld. 2018. Contributions by Interest Groups to Lobbying Coalitions. The Journal of Politics.
  • Ingold, Karin and Philip Leifeld. 2016. 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 26(1): 1-18.
Thursday
  • Leifeld, Philip, Skyler J. Cranmer and Bruce A. Desmarais. 2018. Temporal Exponential Random Graph Models with btergm: Estimation and Bootstrap Confidence Intervals. Journal of Statistical Software.
  • Czarna, Anna Z., Philip Leifeld, Magdalena Śmieja, Michael Dufner and Peter Salovey. 2016. Do Narcissism and Emotional Intelligence Win Us Friends? Modeling Dynamics of Peer Popularity Using Inferential Network Analysis. Personality and Social Psychology Bulletin 42(11): 1588-1599.
  • 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.
  • Leifeld, Philip and Skyler J. Cranmer. 2015. The Temporal Network Autocorrelation Model. Working Paper.
Friday
  • Butts, C. T. (2008). A Relational Event Framework for Social Action. Sociological Methodology, 38(1):155–200.
  • Malang, Thomas, Laurence Brandenberger and Philip Leifeld. 2017. Networks and Social Influence in European Legislative Politics. British Journal of Political Science.
  • Krackhardt, David. 1988. Predicting with networks: Nonparametric multiple regression analysis of dyadic data. Social Networks 10(4):359-381.
  • 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):1090-1098.
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 (Windows, Linux, MacOS).

Literature

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.

The following other ECPR Methods School courses could be useful in combination with this one in a ‘training track .
Recommended Courses Before

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

Additional Information

Disclaimer

The information contained in this course description form may be subject to subsequent adaptations (e.g. taking into account new developments in the field, specific participant demands, group size etc.). Registered participants will be informed in due time in case of adaptations.

Note from the Academic Convenors

By registering to this course, you certify that you possess the prerequisite knowledge that is requested to be able to follow this course. The instructor will not teach these prerequisite items. If you are not sure if you possess this knowledge to a sufficient level, we suggest you contact the instructor before you proceed with your registration.


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