Paper ID: 2205.00639

The Multivariate Community Hawkes Model for Dependent Relational Events in Continuous-time Networks

Hadeel Soliman, Lingfei Zhao, Zhipeng Huang, Subhadeep Paul, Kevin S. Xu

The stochastic block model (SBM) is one of the most widely used generative models for network data. Many continuous-time dynamic network models are built upon the same assumption as the SBM: edges or events between all pairs of nodes are conditionally independent given the block or community memberships, which prevents them from reproducing higher-order motifs such as triangles that are commonly observed in real networks. We propose the multivariate community Hawkes (MULCH) model, an extremely flexible community-based model for continuous-time networks that introduces dependence between node pairs using structured multivariate Hawkes processes. We fit the model using a spectral clustering and likelihood-based local refinement procedure. We find that our proposed MULCH model is far more accurate than existing models both for predictive and generative tasks.

Submitted: May 2, 2022