Robust self supervised symmetric nonnegative matrix factorization to the graph clustering
Abstract Graph clustering is a fundamental task in network analysis, aimed at uncovering meaningful groups of nodes based on structural and attribute-based similarities. Traditional Nonnegative Matrix Factorization (NMF) methods have shown promise in clustering tasks by providing low-dimensional rep...
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-92564-x |
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| author | Yi Ru Michael Gruninger YangLiu Dou |
| author_facet | Yi Ru Michael Gruninger YangLiu Dou |
| author_sort | Yi Ru |
| collection | DOAJ |
| description | Abstract Graph clustering is a fundamental task in network analysis, aimed at uncovering meaningful groups of nodes based on structural and attribute-based similarities. Traditional Nonnegative Matrix Factorization (NMF) methods have shown promise in clustering tasks by providing low-dimensional representations of data. However, most existing NMF-based approaches are highly sensitive to noise and outliers, leading to suboptimal performance in real-world scenarios. Additionally, these methods often struggle to capture the underlying nonlinear structures of complex networks, which can significantly impact clustering accuracy. To address these limitations, this paper introduces Robust Self-Supervised Symmetric NMF (R3SNMF) to improve graph clustering. The proposed algorithm leverages a robust principal component model to handle noise and outliers effectively. By incorporating a self-supervised learning mechanism, R3SNMF iteratively refines the clustering process, enhancing the quality of the learned representations and increasing resilience to data imperfections. The symmetric factorization ensures the preservation of network structures, while the self-supervised approach allows the model to adaptively improve its clustering performance over successive iterations. In addition, R3SNMF integrates a graph-boosting method to improve how relationships within the network are represented. Extensive experimental evaluations on various real-world graph datasets demonstrate that R3SNMF outperforms state-of-the-art clustering methods in terms of both accuracy and robustness. |
| format | Article |
| id | doaj-art-7ca2ee305d0e40c8b63f07508854c688 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-7ca2ee305d0e40c8b63f07508854c6882025-08-20T03:04:12ZengNature PortfolioScientific Reports2045-23222025-03-0115111310.1038/s41598-025-92564-xRobust self supervised symmetric nonnegative matrix factorization to the graph clusteringYi Ru0Michael Gruninger1YangLiu Dou2Department of Mechanical and Industrial Engineering, University of TorontoDepartment of Mechanical and Industrial Engineering, University of TorontoDepartment of Computer Vision Technology (VIS), Baidu IncAbstract Graph clustering is a fundamental task in network analysis, aimed at uncovering meaningful groups of nodes based on structural and attribute-based similarities. Traditional Nonnegative Matrix Factorization (NMF) methods have shown promise in clustering tasks by providing low-dimensional representations of data. However, most existing NMF-based approaches are highly sensitive to noise and outliers, leading to suboptimal performance in real-world scenarios. Additionally, these methods often struggle to capture the underlying nonlinear structures of complex networks, which can significantly impact clustering accuracy. To address these limitations, this paper introduces Robust Self-Supervised Symmetric NMF (R3SNMF) to improve graph clustering. The proposed algorithm leverages a robust principal component model to handle noise and outliers effectively. By incorporating a self-supervised learning mechanism, R3SNMF iteratively refines the clustering process, enhancing the quality of the learned representations and increasing resilience to data imperfections. The symmetric factorization ensures the preservation of network structures, while the self-supervised approach allows the model to adaptively improve its clustering performance over successive iterations. In addition, R3SNMF integrates a graph-boosting method to improve how relationships within the network are represented. Extensive experimental evaluations on various real-world graph datasets demonstrate that R3SNMF outperforms state-of-the-art clustering methods in terms of both accuracy and robustness.https://doi.org/10.1038/s41598-025-92564-xGraph clusteringNonnegative matrix factorizationSymmetric NMFSelf-supervised NMF |
| spellingShingle | Yi Ru Michael Gruninger YangLiu Dou Robust self supervised symmetric nonnegative matrix factorization to the graph clustering Scientific Reports Graph clustering Nonnegative matrix factorization Symmetric NMF Self-supervised NMF |
| title | Robust self supervised symmetric nonnegative matrix factorization to the graph clustering |
| title_full | Robust self supervised symmetric nonnegative matrix factorization to the graph clustering |
| title_fullStr | Robust self supervised symmetric nonnegative matrix factorization to the graph clustering |
| title_full_unstemmed | Robust self supervised symmetric nonnegative matrix factorization to the graph clustering |
| title_short | Robust self supervised symmetric nonnegative matrix factorization to the graph clustering |
| title_sort | robust self supervised symmetric nonnegative matrix factorization to the graph clustering |
| topic | Graph clustering Nonnegative matrix factorization Symmetric NMF Self-supervised NMF |
| url | https://doi.org/10.1038/s41598-025-92564-x |
| work_keys_str_mv | AT yiru robustselfsupervisedsymmetricnonnegativematrixfactorizationtothegraphclustering AT michaelgruninger robustselfsupervisedsymmetricnonnegativematrixfactorizationtothegraphclustering AT yangliudou robustselfsupervisedsymmetricnonnegativematrixfactorizationtothegraphclustering |