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

Hadeel Soliman, Lingfei Zhao, Zhipeng Huang, Subhadeep Paul, Kevin S Xu
Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20329-20346, 2022.

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.

Cite this Paper

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.} }
Endnote
%0 Conference Paper %T The Multivariate Community Hawkes Model for Dependent Relational Events in Continuous-time Networks %A Hadeel Soliman %A Lingfei Zhao %A Zhipeng Huang %A Subhadeep Paul %A Kevin S Xu %B Proceedings of the 39th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2022 %E Kamalika Chaudhuri %E Stefanie Jegelka %E Le Song %E Csaba Szepesvari %E Gang Niu %E Sivan Sabato %F pmlr-v162-soliman22a %I PMLR %P 20329--20346 %U https://proceedings.mlr.press/v162/soliman22a.html %V 162 %X 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.
APA
Soliman, H., Zhao, L., Huang, Z., Paul, S. & Xu, K.S.. (2022). The Multivariate Community Hawkes Model for Dependent Relational Events in Continuous-time Networks. Proceedings of the 39th International Conference on Machine Learning, in Proceedings of Machine Learning Research 162:20329-20346 Available from https://proceedings.mlr.press/v162/soliman22a.html.

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