  There has been great interest in recent years on statistical models for
dynamic networks. In this paper, I propose a stochastic block transition model
(SBTM) for dynamic networks that is inspired by the well-known stochastic block
model (SBM) for static networks and previous dynamic extensions of the SBM.
Unlike most existing dynamic network models, it does not make a hidden Markov
assumption on the edge-level dynamics, allowing the presence or absence of
edges to directly influence future edge probabilities while retaining the
interpretability of the SBM. I derive an approximate inference procedure for
the SBTM and demonstrate that it is significantly better at reproducing
durations of edges in real social network data.
