- Qirong Ho, Le Song, E. Xing. 2011. Evolving Cluster Mixed-Membership Blockmodel for Time-Evolving Networks. Abstract: Time-evolving networks are a natural representation for dynamic social and biological interactions. While latent space models are gaining pop-ularity 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 systemati-cally 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.
