A Nonparametric Bayesian Model for Sparse Dynamic Multigraphs
This work addresses the need for interpretable models in analyzing evolving multigraph data, which is incremental by building on existing edge-exchangeable and dynamic clustering methods.
The authors tackled the problem of modeling sparse, structured temporal interaction multigraphs, such as email communications, by proposing a dynamic nonparametric Bayesian model that combines sparsity with dynamic clustering, resulting in improved held-out likelihood and competitive predictive performance against state-of-the-art dynamic graph models.
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.