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arXiv · 2308.04537

Community Detection in Hypergraphs via Mutual Information Maximization

Jurgen Kritschgau·Daniel Kaiser·Oliver Alvarado Rodriguez·Ilya Amburg·Jessalyn Bolkema·Thomas Grubb·Fangfei Lan·Sepideh Maleki·Phil Chodrow·Bill Kay
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Abstract

The hypergraph community detection problem seeks to identify groups of related nodes in hypergraph data. We propose an information-theoretic hypergraph community detection algorithm which compresses the observed data in terms of community labels and community-edge intersections. This algorithm can also be viewed as maximum-likelihood inference in a degree-corrected microcanonical stochastic blockmodel. We perform the inference/compression step via simulated annealing. Unlike several recent algorithms based on canonical models, our microcanonical algorithm does not require inference of statistical parameters such as node degrees or pairwise group connection rates. Through synthetic experiments, we find that our algorithm succeeds down to recently-conjectured thresholds for sparse random hypergraphs. We also find competitive performance in cluster recovery tasks on several hypergraph data sets.

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Jurgen Kritschgau, Daniel Kaiser, Oliver Alvarado Rodriguez, Ilya Amburg, Jessalyn Bolkema, Thomas Grubb, Fangfei Lan, Sepideh Maleki, Phil Chodrow, Bill Kay. 2023-08-08. Community Detection in Hypergraphs via Mutual Information Maximization. https://arxiv.org/abs/2308.04537

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