  As the availability and importance of temporal interaction data--such as
email communication--increases, it becomes increasingly important to understand
the underlying structure that underpins these interactions. Often these
interactions form a multigraph, where we might have multiple interactions
between two entities. Such multigraphs tend to be sparse yet structured, and
their distribution often evolves over time. Existing statistical models with
interpretable parameters can capture some, but not all, of these properties. We
propose a dynamic nonparametric model for interaction multigraphs that combines
the sparsity of edge-exchangeable multigraphs 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 graph
models.
