Mark S. Handcock

"Degeneracy and Inference for Social Networks Models"

Presented to the Workshop on Statistical Inference, Computing and Visualization for Graphs, Stanford University, August 1-2, 2003.

We consider statistical and stochastic models for random networks that can be used to represent the structural characteristics of the networks. In our applications, the nodes usually represent people, and the edges represent a specified relationship between the people.

To date, the use of complex graph models 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.