Prof. Kevin Lee, Western Michigan University (Hosted by Jun Song)
Title: Clustering Time-Evolving Networks through Temporal ERGMs
Abstract: Model-based clustering of dynamic networks has emerged as one of the increasingly important research topics in statistical network analysis. We present a statistical clustering framework through the temporal exponential-family random graph models (ERGMs). The temporal ERGMs allow the specification of interesting network features (e.g., stability), and the hidden Markov structure allows the inference of the dynamic latent block structure. In this talk, we will introduce two models one with static latent block structure and the other with dynamic latent block structure. Furthermore, we will discuss a variational expectation-maximization algorithm to solve the approximate maximum likelihood estimation. The performance of our proposed methods is demonstrated through an empirical application to the international trade networks.