Modularity Definition and Optimization Algorithm for Community Detection in Signed Hypergraphs

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 hyperg...

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Main Authors: Wei Du, Guangyu Li
Format: Article
Language:English
Published: Wiley 2025-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/cplx/6950334
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author Wei Du
Guangyu Li
author_facet Wei Du
Guangyu Li
author_sort Wei Du
collection DOAJ
description 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.
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publishDate 2025-01-01
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spelling doaj-art-a0c6137aa58e481681c2bfd201caa95d2025-08-20T02:37:20ZengWileyComplexity1099-05262025-01-01202510.1155/cplx/6950334Modularity Definition and Optimization Algorithm for Community Detection in Signed HypergraphsWei Du0Guangyu Li1School of Public Policy and AdministrationSchool of Public Policy and AdministrationThe 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.http://dx.doi.org/10.1155/cplx/6950334
spellingShingle Wei Du
Guangyu Li
Modularity Definition and Optimization Algorithm for Community Detection in Signed Hypergraphs
Complexity
title Modularity Definition and Optimization Algorithm for Community Detection in Signed Hypergraphs
title_full Modularity Definition and Optimization Algorithm for Community Detection in Signed Hypergraphs
title_fullStr Modularity Definition and Optimization Algorithm for Community Detection in Signed Hypergraphs
title_full_unstemmed Modularity Definition and Optimization Algorithm for Community Detection in Signed Hypergraphs
title_short Modularity Definition and Optimization Algorithm for Community Detection in Signed Hypergraphs
title_sort modularity definition and optimization algorithm for community detection in signed hypergraphs
url http://dx.doi.org/10.1155/cplx/6950334
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AT guangyuli modularitydefinitionandoptimizationalgorithmforcommunitydetectioninsignedhypergraphs