Hypermodularity and community detection in hypergraphs
Numerous networked systems feature a structure of nontrivial communities, which often correspond to their functional modules. Such communities have been detected in real-world biological, social, and technological systems, as well as in synthetic models thereof. While much effort has been devoted to...
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| Format: | Article |
| Language: | English |
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American Physical Society
2025-07-01
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| Series: | Physical Review Research |
| Online Access: | http://doi.org/10.1103/58dr-wktc |
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| _version_ | 1849427451319418880 |
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| author | Charo I. del Genio |
| author_facet | Charo I. del Genio |
| author_sort | Charo I. del Genio |
| collection | DOAJ |
| description | Numerous networked systems feature a structure of nontrivial communities, which often correspond to their functional modules. Such communities have been detected in real-world biological, social, and technological systems, as well as in synthetic models thereof. While much effort has been devoted to developing methods for community detection in traditional networks, the study of community structure in networks with higher-order interactions is still not as extensively explored. In this article, we introduce a formalism for the hypermodularity of higher-order networks that allows us to use spectral methods to detect community structures in hypergraphs. We apply this approach to synthetic random networks as well as to real-world data, showing that it produces results that reflect the nature and the dynamics of the interactions modeled, thereby constituting a valuable tool for the extraction of hidden information from complex higher-order data sets. |
| format | Article |
| id | doaj-art-cb240e44bf314fb29ebc064d042eb219 |
| institution | Kabale University |
| issn | 2643-1564 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review Research |
| spelling | doaj-art-cb240e44bf314fb29ebc064d042eb2192025-08-20T03:29:02ZengAmerican Physical SocietyPhysical Review Research2643-15642025-07-017303304510.1103/58dr-wktcHypermodularity and community detection in hypergraphsCharo I. del GenioNumerous networked systems feature a structure of nontrivial communities, which often correspond to their functional modules. Such communities have been detected in real-world biological, social, and technological systems, as well as in synthetic models thereof. While much effort has been devoted to developing methods for community detection in traditional networks, the study of community structure in networks with higher-order interactions is still not as extensively explored. In this article, we introduce a formalism for the hypermodularity of higher-order networks that allows us to use spectral methods to detect community structures in hypergraphs. We apply this approach to synthetic random networks as well as to real-world data, showing that it produces results that reflect the nature and the dynamics of the interactions modeled, thereby constituting a valuable tool for the extraction of hidden information from complex higher-order data sets.http://doi.org/10.1103/58dr-wktc |
| spellingShingle | Charo I. del Genio Hypermodularity and community detection in hypergraphs Physical Review Research |
| title | Hypermodularity and community detection in hypergraphs |
| title_full | Hypermodularity and community detection in hypergraphs |
| title_fullStr | Hypermodularity and community detection in hypergraphs |
| title_full_unstemmed | Hypermodularity and community detection in hypergraphs |
| title_short | Hypermodularity and community detection in hypergraphs |
| title_sort | hypermodularity and community detection in hypergraphs |
| url | http://doi.org/10.1103/58dr-wktc |
| work_keys_str_mv | AT charoidelgenio hypermodularityandcommunitydetectioninhypergraphs |