Social Networks Models: Inference and Degeneracy

Thursday, November 20, 2003 - 9:30am - 10:20am
Keller 3-180
Mark Handcock (University of Washington)
We consider statistical and stochastic models for graphs that can be used to represent the structural characteristics of network. To date, the use of graph models for networks has been limited by three interrelated factors: the complexity of realistic models, paucity of empirically relevant simulation studies, and a poor understanding of the properties of inferential methods. In this talk we discuss solutions to these limitations. We emphasize the important of likelihood-based inferential procedures and role of Markov Chain Monte Carlo (MCMC) algorithms for simulation and inference. A primary ongoing issue is the identification of classes of realistic and parsimonious models. In this regard show the unsuitability of some commonly promoted Markov models classes because they can result in degenerate probability distributions. The ideas are motivated and illustrated by the study of sexual relations networks with the objective of understanding the social determinants of HIV spread.