  We present a Bayesian tensor factorization model for inferring latent group
structures from dynamic pairwise interaction patterns. For decades, political
scientists have collected and analyzed records of the form "country $i$ took
action $a$ toward country $j$ at time $t$"---known as dyadic events---in order
to form and test theories of international relations. We represent these event
data as a tensor of counts and develop Bayesian Poisson tensor factorization to
infer a low-dimensional, interpretable representation of their salient
patterns. We demonstrate that our model's predictive performance is better than
that of standard non-negative tensor factorization methods. We also provide a
comparison of our variational updates to their maximum likelihood counterparts.
In doing so, we identify a better way to form point estimates of the latent
factors than that typically used in Bayesian Poisson matrix factorization.
Finally, we showcase our model as an exploratory analysis tool for political
scientists. We show that the inferred latent factor matrices capture
interpretable multilateral relations that both conform to and inform our
knowledge of international affairs.
