Modularity Based Community Detection in Hypergraphs
<|reference_start|>Modularity Based Community Detection in Hypergraphs: 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.<|reference_end|>
@article{kamiński2024modularity,
    title={Modularity Based Community Detection in Hypergraphs},
    author={Bogumi{l} Kami'nski, Pawe{l} Misiorek, Pawe{l} Pra{l}at and
  Franc{c}ois Th'eberge},
    journal={arXiv preprint arXiv:2406.17556},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.17556},
    primaryClass={cs.SI cs.LG}
}