This course is taught by Filip Agneessens and runs over two weeks (every other day, with a total of 22 contact hours). The workshop offers a practical introduction to cross-sectional ERGM (p* models) and longitudinal SIENA models (SAOM), with a focus on hands-on applications of programs such as MPNET and RSiena and the interpretation of the results.
The course starts with a discussion and overview of statistical inference for complete network analysis, and some simple statistical tests are run in class. We then discuss more complex models, with a specific focus on ERGM (p* models) for cross-sectional social network data and SIENA models (SAOM) for longitudinal social network data. The principles of relational events and related models will also be touched upon (Butts, 2008; Stadtfeld et al., 2017).
The course aims to be interactive, using breakout sessions for the exercises. For the off days, participants will receive homework; which includes running further analyses and interpreting results in small groups. The results will then be discussed again in the next meeting. Please read the software information below about the programs that we will use. The course's primary objective is to ensure participants are able to run and interpret statistical models. However, to allow participants to understand the logic of both ERGM and SIENA models (SAOM), the first 1½ days will focus on the foundations and logic of statistical models and will therefore be more “theoretical”.
Please note this workshop will not be recorded.
The course begins on Tuesday June 30. Morning sessions are from 9:30-11:30 and afternoon sessions are from 1:30-3:30. However, please allow for some extra time at the end of each session in case we run over time.
Tuesday, June 30 (morning only)
§ AM: Introduction and principles of statistical analysis in SNA
§ PM: -home exercises-
Wednesday, July 1
§ AM: ERGM – logic and basics
Robins et al., 2007; Lusher et al., 2013
§ PM: ERGM – logic and basics
Robins et al., 2007; Lusher et al., 2013
Friday, July 3
§ AM: ERGM – running models and refinements
Snijders et al., 2006; Robins et al., 2009
§ PM: ERGM – goodness of fit, further refinements and examples
de la Haye et al., 2017; de Klepper et al., 2017; Wittek et al., 2020
Monday July 6
§ AM: ERGM – advanced topics
Agneessens and Roose, 2008; Wang et al., 2009, 2013; Lubbers and Snijders, 2007
§ PM: SIENA/SAOM – introduction to Siena/SAOM
Snijders et al., 2010
Wednesday, July 8
§ AM: SIENA/SAOM – running and interpreting models in RSiena
Agneessens and Wittek, 2012; Schaefer et al., 2011
§ PM: SIENA/SAOM – running and interpreting models in RSiena
Mercken et al., 2009; Meeussen et al., 2018; Wang et al., 2018
Friday, July 10
§ AM: SIENA/SAOM – advanced topics
Snijders et al., 2013; Snijders and Baerveldt, 2003; Stadtfeld et al., 2020
§ PM: Relational event models and wrap-up
Butts, 2008; Stadtfeld et al., 2017
MPNet for ERGM (http://www.melnet.org.au/pnet/). MPNet.exe is built for Windows. If you have a Mac notebook you might want to ask your IT support how to run Windows (.exe) files on your Mac (if you are unfamiliar with this).
R for running “RSiena” (http://cran.r-project.org/). Download R for Windows or other platform as needed.
Specific packages (RSiena and sna) will best be downloaded from the internet at the start of the course (to ensure everyone has the same version!)
For your own convenience, it might be helpful if you have two monitors (or two pcs) available, so you are simultaneously able to see the programs and “attend” class).
See our software page for any additional details.
Robins, G., P. Pattison, Y. Kalish, and D. Lusher (2007). On exponential random graph models for cross-sectional analysis of complete networks: An introduction to exponential random graph (p*) models for social networks. Social Networks, 29(2): 173-191 [pdf]
Lusher, D., J. Koskinen, and G. Robins (eds.) (2013) Exponential Random Graph Models for Social Networks. Structural Analysis in the Social Sciences. New York: Cambridge University Press.
Snijders, T.A.B., G. van de Bunt, G., and Ch. Steglich (2010). Introduction to stochastic actor-based models for network dynamics. Social Networks, 32: 44-60.