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

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