  We introduce Bayesian Poisson Tucker decomposition (BPTD) for modeling
country--country interaction event data. These data consist of interaction
events of the form "country $i$ took action $a$ toward country $j$ at time
$t$." BPTD discovers overlapping country--community memberships, including the
number of latent communities. In addition, it discovers directed
community--community interaction networks that are specific to "topics" of
action types and temporal "regimes." We show that BPTD yields an efficient MCMC
inference algorithm and achieves better predictive performance than related
models. We also demonstrate that it discovers interpretable latent structure
that agrees with our knowledge of international relations.
