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Modularity Definition and Optimization Algorithm for Community Detection in Signed Hypergraphs

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  • Wei Du
  • Guangyu Li

Abstract

The analysis of super‐dyadic relations through hypergraphs is gradually gaining attention, with its community structure analysis playing a crucial role in computational social science. However, few scholars have paid attention to the impact of hyperedge diversity on the community structure of hypergraphs, especially the impact generated by heterogeneous hyperedges. This paper expands hypergraphs into signed hypergraphs and proposes a framework for community structure in signed hypergraphs along with a variant of modularity. Simultaneously, an optimization algorithm is introduced in this paper to detect potential communities by maximizing modularity. Experimental results reveal that the proposed method can effectively optimize the objective function and detect community structures.

Suggested Citation

  • Wei Du & Guangyu Li, 2025. "Modularity Definition and Optimization Algorithm for Community Detection in Signed Hypergraphs," Complexity, John Wiley & Sons, vol. 2025(1).
    • Handle: RePEc:wly:complx:v:2025:y:2025:i:1:n:6950334
    DOI: 10.1155/cplx/6950334
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    References listed on IDEAS

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    1. Gergely Palla & Imre Derényi & Illés Farkas & Tamás Vicsek, 2005. "Uncovering the overlapping community structure of complex networks in nature and society," Nature, Nature, vol. 435(7043), pages 814-818, June.
    2. Martina Contisciani & Federico Battiston & Caterina De Bacco, 2022. "Inference of hyperedges and overlapping communities in hypergraphs," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    3. Xiaochen He & Ruochen Zhang & Bin Zhu & Hocine Cherifi, 2023. "A Generalized Modularity for Computing Community Structure in Fully Signed Networks," Complexity, Hindawi, vol. 2023, pages 1-20, February.
    4. repec:plo:pone00:0224177 is not listed on IDEAS
    5. Xiaochen He & Haifeng Du & Meng Cai & Marcus W Feldman, 2018. "The evolution of cooperation in signed networks under the impact of structural balance," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-18, October.
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    Cited by:

    1. Tang, Zemiao & Liu, Guolin & Tian, Delong & Zhang, Yu & Qi, Xingqin, 2026. "BGIC: A novel Bi-dimensional Gravitational Influence Centrality method for finding key nodes in signed hypergraphs," Chaos, Solitons & Fractals, Elsevier, vol. 202(P2).

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