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Stochastic Models

Instructor: Filip Agneessens


This 4-day course aims to provide a practical introduction into cross-sectional ERGM (p* models) and longitudinal RSIENA models 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 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 RSIENA for longitudinal social network data.


Preliminary schedule

  • Day 1/Morning: Random graphs and statistical tests
  • Day 1/Afternoon: Intro into ERG models
  • Day 2/Morning: Running ERG (p*) models with PNet
  • Day 2/Afternoon: Focus on interpretation of parameters for different models
  • Day 3/Morning: More interpretation of ERGM and more advanced topics
  • Day 3/Afternoon: Siena models with RSIENA
  • Day 4/Morning: Interpretation of parameters from RSIENA
  • Day 4/Afternoon: Interpretation of parameters from RSIENA and more advanced topics 


  • 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.