Community detection in hypergraphs via mutual information maximization
Document Type
Article
Publication Date
12-1-2024
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.
Identifier
85188442718 (Scopus)
Publication Title
Scientific Reports
External Full Text Location
https://doi.org/10.1038/s41598-024-55934-5
e-ISSN
20452322
PubMed ID
38521798
Issue
1
Volume
14
Grant
DMS 1916439
Fund Ref
American Mathematical Society
Recommended Citation
Kritschgau, Jürgen; Kaiser, Daniel; Alvarado Rodriguez, Oliver; Amburg, Ilya; Bolkema, Jessalyn; Grubb, Thomas; Lan, Fangfei; Maleki, Sepideh; Chodrow, Phil; and Kay, Bill, "Community detection in hypergraphs via mutual information maximization" (2024). Faculty Publications. 74.
https://digitalcommons.njit.edu/fac_pubs/74
