ABSTRACT
Time-evolving networks are a natural representation 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 chapter, 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.
