Modularity Based Community Detection in Hypergraphs
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911932169060352 |
|---|---|
| author | Kamiński, Bogumił Misiorek, Paweł Prałat, Paweł Théberge, François |
| author_facet | Kamiński, Bogumił Misiorek, Paweł Prałat, Paweł Théberge, François |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_17556 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Modularity Based Community Detection in Hypergraphs Kamiński, Bogumił Misiorek, Paweł Prałat, Paweł Théberge, François Social and Information Networks Machine Learning I.6.5; G.4 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. |
| title | Modularity Based Community Detection in Hypergraphs |
| topic | Social and Information Networks Machine Learning I.6.5; G.4 |
| url | https://arxiv.org/abs/2406.17556 |