{"type":"ScholarlyArticle","title":"A Relaxed Symmetric Non-negative Matrix Factorization Approach for Community Discovery (Extended Abstract)","authors":[{"name":"Zhigang Liu","slug":"zhigang-liu","given":"Zhigang","family":"Liu","affiliation":""},{"name":"Hao Yan","slug":"hao-yan","given":"Hao","family":"Yan","affiliation":""},{"name":"Yurong Zhong","slug":"yurong-zhong","given":"Yurong","family":"Zhong","affiliation":""},{"name":"Weiling Li","slug":"weiling-li","given":"Weiling","family":"Li","affiliation":""}],"abstract":"Community discovery is a prominent issue in com-plex network analysis. Symmetric non-negative matrix factorization (SNMF) is frequently adopted to tackle this issue. The use of a single feature matrix can depict network symmetry, but it limits its ability to learn node representations. To break this limitation, we present a novel Relaxed Symmetric NMF (RSN) approach to boost an SNMF-based community detector. It works by 1) expanding the representational space and its degrees of freedom with multiple feature factors; 2) integrating the well-designed equality-constraints to make the model well-aware of the network’s intrinsic symmetry; 3) employing graph regularization to pre-serve the local geometric invariance of the network structure; and 4) separating constraints from decision variables for efficient optimization via the principle of alternating-direction-method of multi-pliers. RSN’s effectiveness is verified through empirical studies on six real social networks, show-casing superior precision in community discovery over existing models and baselines.","doi":"10.24963/ijcai.2025/1216","url":"https://ijcai.org/proceedings/2025/1216","pdf":"https://ijcai.org/proceedings/2025/1216.pdf","markdown":"https://ijcai.org/proceedings/2025/1216.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/1216","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Sister Conferences Best Papers","track":"Sister Conferences Best Papers","number":1216,"pages":{"first":10916,"last":10921},"keywords":["Sister Conferences Best Papers: Machine Learning","Sister Conferences Best Papers: Knowledge Representation and Reasoning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}