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To address this, we propose Relative Entropy-based Regularized Non-negative Matrix Factorization (RENMF), a novel approach that integrates structural and attribute information through advanced matrix factorization techniques. RENMF employs Symmetric NMF and Projective NMF to extract community membership distributions from the structural and attribute spaces, respectively. By treating these distributions as homogeneous, RENMF preserves distinct, denoised information from both spaces while considering their heterogeneous complementary information. We introduce Relative Entropy (RE) as a novel regularization term to facilitate interaction between these spaces, aiming to maximize consistency between the discovered latent distributions. In this interaction, we leverage the asymmetric property of RE to emphasize attributes as essential complementary information for structural clustering. The RENMF model is solved using a new iterative multiplicative update rule, with convergence theoretically proven. We evaluate RENMF\u2019s effectiveness through extensive experiments on 10 real-world networks, comparing it to 11 state-of-the-art clustering methods. The results demonstrate RENMF\u2019s superiority in ground truth matching and key quality metrics, outperforming existing methods.<\/jats:p>","DOI":"10.1145\/3765742","type":"journal-article","created":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T08:58:01Z","timestamp":1756976281000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Relative Entropy-based Regularized Non-negative Matrix Factorization for Attributed Graph Clustering"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4459-0703","authenticated-orcid":false,"given":"Kamal","family":"Berahmand","sequence":"first","affiliation":[{"name":"School of Engineering, RMIT University, Melbourne, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9596-7414","authenticated-orcid":false,"given":"Mehrnoush","family":"Mohammadi","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, The\u00a0University of Queensland, Brisbane, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3119-3349","authenticated-orcid":false,"given":"Razieh","family":"Sheikhpour","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering, Ardakan University, Ardakan, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0517-9420","authenticated-orcid":false,"given":"Mahdi","family":"Jalili","sequence":"additional","affiliation":[{"name":"School of Engineering, RMIT University, Melbourne, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9954-0159","authenticated-orcid":false,"given":"Richi","family":"Nayak","sequence":"additional","affiliation":[{"name":"School of Computer Science, Faculty of Science, Queensland University of Technology, Brisbane, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8664-6117","authenticated-orcid":false,"given":"Hassan","family":"Khosravi","sequence":"additional","affiliation":[{"name":"Institute for Teaching and Learning Innovation, The University of Queensland, Brisbane, Australia"}]}],"member":"320","published-online":{"date-parts":[[2025,10,7]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3681793"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119200"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3690624.3709387"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/3542954.3543044"},{"issue":"5","key":"e_1_3_1_6_2","first-page":"btae293","article-title":"ScTPC: A novel semisupervised deep clustering model for scRNA-seq data","volume":"40","author":"Qiu Yushan","year":"2024","unstructured":"Yushan Qiu, Lingfei Yang, Hao Jiang, and Quan Zou. 2024. 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