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Based on the network topology, the ORSNMF algorithm models the differences of the outside of the community, the similarities of the nodes inside the community, and the sparseness of the community membership matrices at the same time, which together guides the iterative learning process to better reflect the underlying information and inherent attributes of the community structure in order to improve the correct rate of dividing subgroups. An algorithm with convergence guarantee is also proposed to solve the model, and finally a large number of comparative experiments are conducted, and the results show that the algorithm has good results.<\/jats:p>","DOI":"10.1007\/s40747-024-01404-4","type":"journal-article","created":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T02:08:35Z","timestamp":1711937315000},"page":"4697-4712","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Multi-constraint non-negative matrix factorization for community detection: orthogonal regular sparse constraint non-negative matrix factorization"],"prefix":"10.1007","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4754-7049","authenticated-orcid":false,"given":"Zigang","family":"Chen","sequence":"first","affiliation":[]},{"given":"Qi","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Tao","family":"Leng","sequence":"additional","affiliation":[]},{"given":"Zhenjiang","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Ding","family":"Pan","sequence":"additional","affiliation":[]},{"given":"Yuhong","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Xiaoyong","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,1]]},"reference":[{"key":"1404_CR1","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1016\/j.ins.2017.07.012","volume":"417","author":"Z Zheng","year":"2017","unstructured":"Zheng Z, Ye F, Li R-H, Ling G, Jin T (2017) Finding weighted k-truss communities in large networks. 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