{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:15:12Z","timestamp":1758672912166,"version":"3.44.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>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\u2019s 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\u2019s effectiveness is verified through empirical studies on six real social networks, show-casing superior precision in community discovery over existing models and baselines.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/1216","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"10916-10921","source":"Crossref","is-referenced-by-count":0,"title":["A Relaxed Symmetric Non-negative Matrix Factorization Approach for Community Discovery (Extended Abstract)"],"prefix":"10.24963","author":[{"given":"Zhigang","family":"Liu","sequence":"first","affiliation":[{"name":"Dongguan University of Technology"},{"name":"University of Electronic Science and Technology of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Yan","sequence":"additional","affiliation":[{"name":"Dongguan University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yurong","family":"Zhong","sequence":"additional","affiliation":[{"name":"Dongguan University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiling","family":"Li","sequence":"additional","affiliation":[{"name":"Dongguan University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2025","name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","start":{"date-parts":[[2025,8,16]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:36:28Z","timestamp":1758627388000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/1216"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/1216","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}