تقرير
Community Detection in Hypergraphs via Mutual Information Maximization
| العنوان: | Community Detection in Hypergraphs via Mutual Information Maximization |
|---|---|
| المؤلفون: | Kritschgau, Jurgen, Kaiser, Daniel, Rodriguez, Oliver Alvarado, Amburg, Ilya, Bolkema, Jessalyn, Grubb, Thomas, Lan, Fangfei, Maleki, Sepideh, Chodrow, Phil, Kay, Bill |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science Mathematics |
| مصطلحات موضوعية: | Computer Science - Discrete Mathematics, Computer Science - Social and Information Networks, Mathematics - Combinatorics, Mathematics - Optimization and Control |
| الوصف: | 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. Comment: Submitted |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2308.04537 |
| رقم الأكسشن: | edsarx.2308.04537 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |