Sequential Edge Clustering in Temporal Multigraphs
This work addresses the need for better temporal modeling in sparse interaction graphs like email or transaction networks, though it appears incremental as it builds on existing exchangeable models.
The authors tackled the problem of modeling temporal dynamics in sparse, unbounded interaction graphs by proposing a dynamic nonparametric model that combines sparsity with clustering patterns reinforcing recent behavior, resulting in improved held-out likelihood and impressive predictive performance against state-of-the-art models.
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.