{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/constraint-induced-symmetric-nonnegative","title":"Constraint-Induced Symmetric Nonnegative Matrix Factorization for Accurate Community Detection","arxiv_id":null,"date":"2023-01-01","proceeding":"journal 2023 1","authors":["ZhiGang Liu","Xin Luo","Zidong Wang","Xiaohui Liu"],"abstract":"As a fundamental characteristic of an undirected network, community reveals its networking organization and\r\nfunctional mechanisms, making community detection be a highly-interesting issue in network representation\r\nlearning. With great interpretability, a symmetric and nonnegative matrix factorization (SNMF)-based approach\r\nis frequently adopted to tackle this issue. However, it only adopts a unique feature matrix for describing the\r\nsymmetry of an undirected network, which unfortunately results in a reduced feature space that evidently impairs its representation learning ability. Motivated by this discovery, this paper proposes a novel Constraintinduced Symmetric Nonnegative Matrix Factorization (C-SNMF) model that adopts three-fold ideas: a) Representing a target undirected network with multiple latent feature matrices, thus preserving its representation\r\nlearning capacity; b) Incorporating a symmetry-regularizer into its objective function, which preserves the\r\nsymmetry of the learnt low-rank approximation to the adjacency matrix, thereby making the resultant detector\r\nprecisely illustrate the target network’s symmetry; and c) Introducing a graph-regularizer that preserves local\r\ninvariance of the network’s intrinsic geometry into its learning objective, thus making the achieved detector\r\nwell-aware of community structure within the target network. Note that the regularization coefficients are\r\nselected according to the modularity of the learnt community structure on the training data only, thereby greatly\r\nimproving the achieved model’s practical significance for real applications. Experimental results on six realworld networks demonstrate that the proposed C-SNMF model significantly outperforms the benchmarks and\r\nstate-of-the-art models in achieving highly-accurate community detection results.","url_abs":"https://www.sciencedirect.com/science/article/pii/S1566253522001300","url_pdf":"https://www.sciencedirect.com/science/article/pii/S1566253522001300","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"constraint-induced-symmetric-nonnegative","repo_url":"https://github.com/2024-MindSpore-1/Code1/tree/main/luoxin/CSNMF-MindSpore/CSNMF","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"community-detection","task_name":"Community Detection"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}