Stochastic Models
The workshop offers a practical introduction to cross-sectional ERGM (p* models) and longitudinal SIENA models (SAOM) using R. The course uses an 'inverted-classroom' structure. Participants will get access to a series of videos covering the theoretical basics of the model and the application in R. We will meet as a group twice to discuss questions and go through a model application together. Participants will be split into smaller groups based on their specific fields of study, data, and research questions and will get individual support with their specific applications. These small groups meet twice. Dates and times will be arranged based on availability of the members of each group.
Participants are expected to have
read the software information below about the programs that we will use,
read the papers below,
and watch the videos that will be made available upon registration.
Prior familiarity with R is very helpful. I recommend the free online book R for Data Science (https://r4ds.hadley.nz/).
Software
Download R and RStudio for Windows or another platform as needed. Links are https://cran.r-project.org/index.html and https://posit.co/downloads/ respectively. Please make sure that your R-version is up-to-date (at least, 4.3.3).
In R, the packages (RSiena, statnet, and Bergm) 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).
Readings (expected)
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.
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.
Readings (additional)
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.
Caimo, A., & Friel, N. (2011). Bayesian inference for exponential random graph models. Social networks, 33(1), 41-55.
Caimo, A., Bouranis, L., Krause, R., & Friel, N. (2021). Statistical network analysis with Bergm. Journal of Statistical Software 104(1), 1-23.
Lusher, D., J. Koskinen, and G. Robins (eds.) (2013) Exponential Random Graph Models or Social Networks. Structural Analysis in the Social Sciences. New York:
Cambridge University Press.
Kalish, Y. (2020). Stochastic actor-oriented models for the co-evolution of networks and behavior: An introduction and tutorial. Organizational Research Methods, 23(3), 511-534.
Krause, R. W., Huisman, M., Steglich, C., & Snijders, T. (2020). Missing data incross-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.
Instructor contact information
Robert Krause <Robert.w.Krause@uky.edu>
