CHIP: A Hawkes Process Model for Continuous-time Networks with Scalable and Consistent Estimation

Part of Advances in Neural Information Processing Systems 33 (NeurIPS 2020)

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Makan Arastuie, Subhadeep Paul, Kevin Xu


In many application settings involving networks, such as messages between users of an on-line social network or transactions between traders in financial markets, the observed data consist of timestamped relational events, which form a continuous-time network. We propose the Community Hawkes Independent Pairs (CHIP) generative model for such networks. We show that applying spectral clustering to an aggregated adjacency matrix constructed from the CHIP model provides consistent community detection for a growing number of nodes and time duration. We also develop consistent and computationally efficient estimators for the model parameters. We demonstrate that our proposed CHIP model and estimation procedure scales to large networks with tens of thousands of nodes and provides superior fits than existing continuous-time network models on several real networks.