Stochastic Models
Overview
This course is taught by Filip Agneessens and Robert Krause and runs with two sessions on instructional days and is spread across two weeks from (June 20-29) for a total of 22.5 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.
The course aims to be interactive, using breakout sessions for the exercises. 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 day will focus on the foundations and logic of statistical models and will therefore be more “theoretical”.
Please note this workshop will not be recorded. Familiarity with R (for running RSiena) is not required.
The course begins on Monday, June 20. June 20, 21, 22 and 27 will have morning sessions from 10:00am-12:15 EST and afternoon sessions from 12:45-3:00pm. On June 28 and 29 there will be only afternoon session (12:45-3:00pm). However, please allow for some extra time at the end of the afternoon session in case we run over time.
Below, you will find a tentative schedule:
Monday, June 20
§ AM - 10:00am-12:15: Introduction and principles of statistical analysis in SNA
Agneessens, 2020; Borgatti, Everett, Johnson and Agneessens, 2022 (CH14)
§ PM - 12:45-3:00pm: ERGM – logic and basics
Robins et al., 2007; Lusher et al., 2013; Borgatti, Everett, Johnson and Agneessens, 2022 (CH15)
Tuesday, June 21
§ AM - 10:00am-12:15: ERGM – running models and refinements
Snijders et al., 2006; Robins et al., 2009
§ PM - 12:45-3:00pm: ERGM – goodness of fit, further refinements and examples
de la Haye et al., 2017; de Klepper et al., 2017
Wednesday, June 22
§ AM - 10:00am-12:15: ERGM – advanced topics - goodness of fit and improvements of models
Agneessens and Roose, 2008; Wang et al., 2009, 2013; Lubbers and Snijders, 2007
§ PM - 12:45-3:00pm: ERGM – advanced topics - two-mode, multiple groups and multilevel
Lubbers and Snijders, 2007; Snijders, 2015; Wittek et al., 2020
Monday, June 27
§ AM - 10:00am-12:15: SIENA/SAOM – introduction to Siena
Snijders et al., 2010
§ PM - 12:45-3:00pm: SIENA/SAOM – running and interpreting models in RSiena
Snijders et al., 2010; Agneessens and Wittek, 2012; Schaefer et al., 2011
Tuesday, June 28
§ PM - 12:45-3:00pm: SIENA/SAOM – advanced topics
Snijders et al., 2013; Snijders and Baerveldt, 2003
Tuesday, June 29
§ PM - 12:45-3:00pm: SIENA/SAOM – advanced topics, including missing data
Krause et al., 2020; Krause et al., 2018
Software
R for running "statnet" and “RSiena” (http://cran.r-project.org/). Download R for Windows or other platform as needed.
Specific packages (RSiena and statnet/sna) will best be downloaded from the internet at the start of the course (to ensure everyone has the same version!). Detailed instructions will follow by email a week before the start of class.
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.
Readings
Agneessens, F. (2020). Dyadic, nodal and group-level approaches to study the antecedents and consequences of networks: Which social network models to use and when. In The Oxford Handbook of Social Networks. Oxford University Press.
Borgatti, S. P., Everett, M. G., Johnson, J. C., & Agneessens, F. (2022). Analyzing Social Networks Using R. SAGE.
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.
Krause, R. W., Huisman, M., Steglich, C., & Snijders, T. (2020). Missing data in cross-sectional networks–An extensive comparison of missing data treatment methods. Social Networks, 62, 99-112.
Krause, R. W., Huisman, M., & Snijders, T. A. (2018). Multiple imputation for longitudinal network data. Statistica Applicata-Italian Journal of Applied Statistics, (1), 33-57.
