Modularity Based Community Detection in Hypergraphs

• URL: http://arxiv.org/abs/2406.17556v1
• Date: Tue, 25 Jun 2024 13:49:56 GMT
• Title: Modularity Based Community Detection in Hypergraphs
• Authors: Bogumił Kamiński, Paweł Misiorek, Paweł Prałat, François Théberge,
• Abstract summary: We propose a scalable community detection algorithm using hypergraph modularity function, h-Louvain. It is an adaptation of the classical Louvain algorithm in the context of hypergraphs.
• Score: 1.4999444543328293
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose a scalable community detection algorithm using hypergraph modularity function, h-Louvain. It is an adaptation of the classical Louvain algorithm in the context of hypergraphs. We observe that a direct application of the Louvain algorithm to optimize the hypergraph modularity function often fails to find meaningful communities. We propose a solution to this issue by adjusting the initial stage of the algorithm via carefully and dynamically tuned linear combination of the graph modularity function of the corresponding two-section graph and the desired hypergraph modularity function. The process is guided by Bayesian optimization of the hyper-parameters of the proposed procedure. Various experiments on synthetic as well as real-world networks are performed showing that this process yields improved results in various regimes.

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