Community detection on simplicial complexes
Abstract Recent advances in complex systems have highlighted the utility of simplicial complexes for modeling higher-order interactions, particularly in biological and physical networks. This study presents enhanced Simplex2Vec, an adaptation of the Simplex2Vec algorithm, to facilitate community det...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-07-01
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| Series: | Applied Network Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s41109-025-00695-x |
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| Summary: | Abstract Recent advances in complex systems have highlighted the utility of simplicial complexes for modeling higher-order interactions, particularly in biological and physical networks. This study presents enhanced Simplex2Vec, an adaptation of the Simplex2Vec algorithm, to facilitate community detection within such structures. We compare enhanced Simplex2Vec’s efficacy against the Leiden algorithm and Spectral clustering using 7 distinct datasets with different structure. Our results indicate that enhanced Simplex2Vec generally outperforms other methods in detecting nuanced community structures, i.e subnetworks where higher-order simplices significantly shape community boundaries rather than just dense pairwise links, except in Normalized Mutual Information (NMI) for the Karate Club graph, where the Louvain method shows superior performance. These findings suggest enhanced Simplex2Vec’s potential as a robust tool for community detection in simplicial complexes, with implications for various scientific domains. |
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| ISSN: | 2364-8228 |