  Interaction graphs, such as those recording emails between individuals or
transactions between institutions, tend to be sparse yet structured, and often
grow in an unbounded manner. Such behavior can be well-captured by structured,
nonparametric edge-exchangeable graphs. However, such exchangeable models
necessarily ignore temporal dynamics in the network. We propose a dynamic
nonparametric model for interaction graphs that combine the sparsity of the
exchangeable models with dynamic clustering patterns that tend to reinforce
recent behavioral patterns. We show that our method yields improved held-out
likelihood over stationary variants, and impressive predictive performance
against a range of state-of-the-art dynamic interaction graph models.
