Community detection in hypergraphs via mutual information maximization

Kritschgau, Jürgen; Kaiser, Daniel; Alvarado Rodriguez, Oliver; Amburg, Ilya; Bolkema, Jessalyn; Grubb, Thomas; Lan, Fangfei; Maleki, Sepideh; Chodrow, Phil; Kay, Bill

doi:10.1038/s41598-024-55934-5

Title: Community detection in hypergraphs via mutual information maximization

Journal Article · 22 March 2024 · Scientific Reports

DOI: https://doi.org/10.1038/s41598-024-55934-5 · OSTI ID:2327054

Kritschgau, Jürgen; Kaiser, Daniel; Alvarado Rodriguez, Oliver; Amburg, Ilya; Bolkema, Jessalyn; Grubb, Thomas; Lan, Fangfei; Maleki, Sepideh; Chodrow, Phil; Kay, Bill

Abstract The hypergraph community detection problem seeks to identify groups of related vertices 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 compression/inference step via simulated annealing. Unlike several recent algorithms based on canonical models, our microcanonical algorithm does not require inference of statistical parameters such as vertex 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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Sponsoring Organization:: USDOE

Grant/Contract Number:: AC05-76RL01830

OSTI ID:: 2327054

Alternate ID(s):: OSTI ID: 2438439

Journal Information:: Scientific Reports, Journal Name: Scientific Reports Journal Issue: 1 Vol. 14; ISSN 2045-2322

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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