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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.07301 |
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| _version_ | 1866911950707884032 |
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| author | Xiao, Jing Wei, Ya-Wei Xu, Xiao-Ke |
| author_facet | Xiao, Jing Wei, Ya-Wei Xu, Xiao-Ke |
| contents | Higher-order community detection (HCD) reveals both mesoscale structures and functional characteristics of real-life networks. Although many methods have been developed from diverse perspectives, to our knowledge, none can provide fine-grained higher-order fuzzy community information. This study presents a novel concept of higher-order fuzzy memberships that quantify the membership grades of motifs to crisp higher-order communities, thereby revealing the partial community affiliations. Furthermore, we employ higher-order fuzzy memberships to enhance HCD via a general framework called fuzzy memberships assisted motif-based evolutionary modularity (FMMEM). In FFMEM, on the one hand, a fuzzy membership-based neighbor community modification (FM-NCM) strategy is designed to correct misassigned bridge nodes, thereby improving partition quality. On the other hand, a fuzzy membership-based local community merging (FM-LCM) strategy is also proposed to combine excessively fragmented communities for enhancing local search ability. Experimental results indicate that the FMMEM framework outperforms state-of-the-art methods in both synthetic and real-world datasets, particularly in the networks with ambiguous and complex structures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_07301 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Higher-order Fuzzy Membership in Motif Modularity Optimization Xiao, Jing Wei, Ya-Wei Xu, Xiao-Ke Physics and Society Higher-order community detection (HCD) reveals both mesoscale structures and functional characteristics of real-life networks. Although many methods have been developed from diverse perspectives, to our knowledge, none can provide fine-grained higher-order fuzzy community information. This study presents a novel concept of higher-order fuzzy memberships that quantify the membership grades of motifs to crisp higher-order communities, thereby revealing the partial community affiliations. Furthermore, we employ higher-order fuzzy memberships to enhance HCD via a general framework called fuzzy memberships assisted motif-based evolutionary modularity (FMMEM). In FFMEM, on the one hand, a fuzzy membership-based neighbor community modification (FM-NCM) strategy is designed to correct misassigned bridge nodes, thereby improving partition quality. On the other hand, a fuzzy membership-based local community merging (FM-LCM) strategy is also proposed to combine excessively fragmented communities for enhancing local search ability. Experimental results indicate that the FMMEM framework outperforms state-of-the-art methods in both synthetic and real-world datasets, particularly in the networks with ambiguous and complex structures. |
| title | Higher-order Fuzzy Membership in Motif Modularity Optimization |
| topic | Physics and Society |
| url | https://arxiv.org/abs/2407.07301 |