Detecting memberships in multiplex networks via nonnegative matrix factorization and tensor decomposition
Multiplex networks provide a powerful data structure for capturing diverse relationships among nodes, and the challenge of community detection within these networks has recently attracted considerable attention. We propose a general and flexible generative model-the mixed membership multilayer stoch...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | New Journal of Physics |
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| Online Access: | https://doi.org/10.1088/1367-2630/ada573 |
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| _version_ | 1841525430921723904 |
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| author | Fengqin Tang Xiaozong Wang Xuejing Zhao Chunning Wang |
| author_facet | Fengqin Tang Xiaozong Wang Xuejing Zhao Chunning Wang |
| author_sort | Fengqin Tang |
| collection | DOAJ |
| description | Multiplex networks provide a powerful data structure for capturing diverse relationships among nodes, and the challenge of community detection within these networks has recently attracted considerable attention. We propose a general and flexible generative model-the mixed membership multilayer stochastic block model, in which layers with meaningful similarities are grouped together. Within each layer group, the layers share the same mixed membership assignments of nodes to communities, but with distinct community link probability matrices. To address this, we developed non-negative matrix factorization and tensor decomposition (NMFTD), a joint clustering approach, to identify cohesive layer groups and determine the mixed memberships of nodes within them. Our method first clusters the layers using matrix factorization with graph regularization, followed by a tensor decomposition strategy enhanced by a corner-finding algorithm to uncover the nodes’ mixed memberships in each group. The proposed method is asymptotically consistent, and its effectiveness is validated through experiments on synthetic and real-world multilayer networks. The results show that NMFTD exhibits robustness across various parameter settings, outperforming or competing closely with other methods. |
| format | Article |
| id | doaj-art-074725455fa04bddbcd20d2fbe46fbb6 |
| institution | Kabale University |
| issn | 1367-2630 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | New Journal of Physics |
| spelling | doaj-art-074725455fa04bddbcd20d2fbe46fbb62025-01-17T12:42:55ZengIOP PublishingNew Journal of Physics1367-26302025-01-0127101300710.1088/1367-2630/ada573Detecting memberships in multiplex networks via nonnegative matrix factorization and tensor decompositionFengqin Tang0Xiaozong Wang1Xuejing Zhao2Chunning Wang3School of Mathematics and Statistics, Huaibei Normal University , Huaibei, People’s Republic of ChinaSchool of Mathematics and Statistics, Huaibei Normal University , Huaibei, People’s Republic of ChinaSchool of Mathematics and Statistics, Lanzhou University , Lanzhou, People’s Republic of ChinaSchool of Statistics and Data Science, Lanzhou University of Finance and Economics , Lanzhou, People’s Republic of ChinaMultiplex networks provide a powerful data structure for capturing diverse relationships among nodes, and the challenge of community detection within these networks has recently attracted considerable attention. We propose a general and flexible generative model-the mixed membership multilayer stochastic block model, in which layers with meaningful similarities are grouped together. Within each layer group, the layers share the same mixed membership assignments of nodes to communities, but with distinct community link probability matrices. To address this, we developed non-negative matrix factorization and tensor decomposition (NMFTD), a joint clustering approach, to identify cohesive layer groups and determine the mixed memberships of nodes within them. Our method first clusters the layers using matrix factorization with graph regularization, followed by a tensor decomposition strategy enhanced by a corner-finding algorithm to uncover the nodes’ mixed memberships in each group. The proposed method is asymptotically consistent, and its effectiveness is validated through experiments on synthetic and real-world multilayer networks. The results show that NMFTD exhibits robustness across various parameter settings, outperforming or competing closely with other methods.https://doi.org/10.1088/1367-2630/ada573community detectionmultiplex networktensor decompositionmixed membership multiplex stochastic block model |
| spellingShingle | Fengqin Tang Xiaozong Wang Xuejing Zhao Chunning Wang Detecting memberships in multiplex networks via nonnegative matrix factorization and tensor decomposition New Journal of Physics community detection multiplex network tensor decomposition mixed membership multiplex stochastic block model |
| title | Detecting memberships in multiplex networks via nonnegative matrix factorization and tensor decomposition |
| title_full | Detecting memberships in multiplex networks via nonnegative matrix factorization and tensor decomposition |
| title_fullStr | Detecting memberships in multiplex networks via nonnegative matrix factorization and tensor decomposition |
| title_full_unstemmed | Detecting memberships in multiplex networks via nonnegative matrix factorization and tensor decomposition |
| title_short | Detecting memberships in multiplex networks via nonnegative matrix factorization and tensor decomposition |
| title_sort | detecting memberships in multiplex networks via nonnegative matrix factorization and tensor decomposition |
| topic | community detection multiplex network tensor decomposition mixed membership multiplex stochastic block model |
| url | https://doi.org/10.1088/1367-2630/ada573 |
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