The Multivariate Community Hawkes Model for Dependent Relational Events in Continuous-time Networks
Hadeel Soliman, Lingfei Zhao, Zhipeng Huang, Subhadeep Paul, Kevin S Xu
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.
BibTeX
@InProceedings{pmlr-v162-soliman22a,
title = {The Multivariate Community {H}awkes Model for Dependent Relational Events in Continuous-time Networks},
author = {Soliman, Hadeel and Zhao, Lingfei and Huang, Zhipeng and Paul, Subhadeep and Xu, Kevin S},
booktitle = {Proceedings of the 39th International Conference on Machine Learning},
pages = {20329--20346},
year = {2022},
editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
volume = {162},
series = {Proceedings of Machine Learning Research},
month = {17--23 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v162/soliman22a/soliman22a.pdf},
url = {https://proceedings.mlr.press/v162/soliman22a.html},
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.}
}