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Article

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Title

Modularity based community detection in hypergraphs

Authors

[ 1 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication

2024

Published in

Journal of Complex Networks

Journal year: 2024 | Journal volume: vol. 12 | Journal number: iss. 5

Article type

scientific article

Publication language

english

Keywords
EN
  • hypergraph
  • communities
  • clusters
  • modularity
Abstract

EN 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.

Date of online publication

21.10.2024

Pages (from - to)

cnae041-1 - cnae041-26

DOI

10.1093/comnet/cnae041

URL

https://academic.oup.com/comnet/article/12/5/cnae041/7829128

Comments

Article Number: cnae041

License type

CC BY (attribution alone)

Open Access Mode

czasopismo hybrydowe

Open Access Text Version

final published version

Date of Open Access to the publication

at the time of publication

Ministry points / journal

100

Impact Factor

2,2 [List 2023]

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