  Dynamic modeling of longitudinal networks has been an increasingly important
topic in applied research. While longitudinal network data commonly exhibit
dramatic changes in its structures, existing methods have largely focused on
modeling smooth topological changes over time. In this paper, we develop a
hidden Markov multilinear tensor model (HMTM) that combines the multilinear
tensor regression model (Hoff 2011) with a hidden Markov model using Bayesian
inference. We model changes in network structure as shifts in discrete states
yielding particular sets of network generating parameters. Our simulation
results demonstrate that the proposed method correctly detects the number,
locations, and types of changes in latent node characteristics. We apply the
proposed method to international military alliance networks to find structural
changes in the coalition structure among nations.
