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

• URL: http://arxiv.org/abs/2205.00639v1
• Date: Mon, 2 May 2022 04:08:44 GMT
• Title: The Multivariate Community Hawkes Model for Dependent Relational Events in Continuous-time Networks
• Authors: Hadeel Soliman, Lingfei Zhao, Zhipeng Huang, Subhadeep Paul, Kevin S. Xu
• Abstract summary: The block model (SBM) is one of the most widely used generative models for network data. We propose the multivariate community Hawkes (MULCH) model, an extremely flexible community-based model for continuous-time networks. We find that our proposed MULCH model is far more accurate than existing models both for predictive and predictive generative tasks.
• Score: 3.55528500800612
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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