Discrete temporal models of social networks

Authors:
Steve Hanneke;Eric P. Xing
Affiliations:
Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA;Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA
Venue:
ICML'06 Proceedings of the 2006 conference on Statistical network analysis
Year:
2006

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Abstract

We propose a family of statistical models for social network evolution over time, which represents an extension of Exponential Random Graph Models (ERGMs). Many of the methods for ERGMs are readily adapted for these models, including MCMC maximum likelihood estimation algorithms. We discuss models of this type and give examples, as well as a demonstration of their use for hypothesis testing and classification.