Sessions‎ > ‎

Stochastic Models

Instructor: Filip Agneessens

Overview

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.

Software

  • MPNet for ERGM (http://www.melnet.org.au/pnet/). MPNet.exe is a build for Windows. However, 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 SIENA and the stochastic tests (http://cran.r-project.org/
  • Download R for Windows or other platform as needed. Specific packages (RSIENA and SNA) will best be downloaded from internet at the start of the course (to ensure everyone has the same version!). If needed they can be put on a usb-stick.

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 

Readings

  • 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.