Advanced methods for social network analysis

Course Summary

We begin by looking at ERGMs (Exponential Random Graph Models) using the software package PNET and also statnet in R. This allows us to answer questions such as: Are there more triads in my network than I would expect by chance? And more complex questions involving attributes such as am I more likely to be friends with someone who is a similar age to me? The course moves on to longitudinal data using the R version of the SIENA package. This looks at network formation over time and is an actor based model that allows for endogenous network effects (such as transitivity and popularity) as well actor attributes (such as homophily) to be included in the model. Finally the course will cover the use of permutation tests and some advanced descriptive methods which will depend on the participants interests.

The course will

  1. Introduce the theory and terminology of the Exponential Random Graph Model (ERGM) and show how it can be applied to network data using PNET and statnet and discuss issues such as convergence, degeneracy and goodness of fit.
  2. Extend the ERGM to deal with attribute data and show how the model can be used in practice.
  3. Describe the actor based model implemented in RSiena
  4. Show how the model can be extended by using a variety of practical examples with an emphasis on interpretation of the output.
  5. Describe the use of permutation tests for network analysis.

Preliminary reading

  • Robins, G L, Pattison, P E, Kalish, Y, Lusher, D (2007) An introduction to exponential random graph (p * ) models for social networks Social Networks. 29:173-191.
  • Snijders, T.A.B., Doreian, P. (2010). Introduction to the special issue on network dynamics. Social Networks, 32, 1-3.
  • Snijders, T.A.B., Steglich, C.E.G., and van de Bunt, G.G. (2010). Introduction to actor-based models for network dynamics. Social Networks, 32, 44-60.

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