Date of Original Version

4-2011

Type

Conference Proceeding

Rights Management

Copyright 2011 by the authors

Abstract or Description

Time-evolving networks are a natural presentation for dynamic social and biological interactions. While latent space models are gaining popularity in network modeling and analysis, previous works mostly ignore networks with temporal behavior and multi-modal actor roles. Furthermore, prior knowledge, such as division and grouping of social actors or biological specificity of molecular functions, has not been systematically exploited in network modeling. In this paper, we develop a network model featuring a state space mixture prior that tracks complex actor latent role changes through time. We provide a fast variational inference algorithm for learning our model, and validate it with simulations and held-out likelihood comparisons on real-world time-evolving networks. Finally, we demonstrate our model's utility as a network analysis tool, by applying it to United States Congress voting data.

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Published In

Journal of Machine Learning Research : Workshop and Conference Proceedings, 15, 342-350.