  This paper addresses the problem of entity alignment in attribute-rich
event-driven graphs. Unlike many other entity alignment problems, we are
interested in aligning entities based on the similarity of their actions, i.e.,
entities that participate in similar events are more likely to be the same. We
model the generative process of this problem as a Bayesian model and derive our
proposed algorithm from the posterior predictive distribution. We apply our
Hierarchical Entity AlignmenT (HEAT) algorithm to two datasets, one on
publications and the other on financial transactions, derived from real data
and provided to us by an external collaborator.
