{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T13:56:59Z","timestamp":1772373419076,"version":"3.50.1"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the National Key Research and Development Program of China","award":["2022YFB3102904"],"award-info":[{"award-number":["2022YFB3102904"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1007\/s10115-026-02710-8","type":"journal-article","created":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T13:16:02Z","timestamp":1772370962000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Evo-NMTF: dynamic community detection via dual-path evolutionary nonnegative matrix tri-factorization"],"prefix":"10.1007","volume":"68","author":[{"given":"Xilong","family":"Li","sequence":"first","affiliation":[]},{"given":"Yan","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,3,1]]},"reference":[{"key":"2710_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2024.108037","volume":"202","author":"Y Chen","year":"2025","unstructured":"Chen Y, Fang K, Lan W et al (2025) Community influence analysis in social networks. Comput Stat Data Anal 202:108037. https:\/\/doi.org\/10.1016\/j.csda.2024.108037","journal-title":"Comput Stat Data Anal"},{"issue":"1","key":"2710_CR2","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1109\/TCBB.2021.3138142","volume":"20","author":"I Manipur","year":"2023","unstructured":"Manipur I, Giordano M, Piccirillo M et al (2023) Community detection in protein\u2013protein interaction networks and applications. IEEE\/ACM Trans Comput Biol Bioinf 20(1):217\u2013237. https:\/\/doi.org\/10.1109\/TCBB.2021.3138142","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"issue":"1","key":"2710_CR3","doi-asserted-by":"publisher","first-page":"78","DOI":"10.26599\/BDMA.2024.9020050","volume":"8","author":"A Galdeman","year":"2025","unstructured":"Galdeman A, Zignani M, Gaito S (2025) Graph evolution rules meet communities: assessing global and local patterns in the evolution of dynamic networks. Big Data Min Anal 8(1):78\u2013102. https:\/\/doi.org\/10.26599\/BDMA.2024.9020050","journal-title":"Big Data Min Anal"},{"issue":"1","key":"2710_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s41109-024-00687-3","volume":"10","author":"ND Conrad","year":"2025","unstructured":"Conrad ND, Tonello E, Zonker J, Siebert H (2025) Detection of dynamic communities in temporal networks with sparse data. Appl Netw Sci 10(1):1\u201329. https:\/\/doi.org\/10.1007\/s41109-024-00687-3","journal-title":"Appl Netw Sci"},{"issue":"4","key":"2710_CR5","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1145\/1514888.1514891","volume":"3","author":"Y Lin","year":"2009","unstructured":"Lin Y, Chi Y, Zhu S, Sundaram H, Tseng BL (2009) Analyzing communities and their evolutions in dynamic social networks. ACM Trans Knowl Discov Data 3(4):8\u20131831. https:\/\/doi.org\/10.1145\/1514888.1514891","journal-title":"ACM Trans Knowl Discov Data"},{"key":"2710_CR6","doi-asserted-by":"publisher","unstructured":"Sun JM, Papadimitriou S, Yu PS, Faloutsos C (2007) Graphscope: parameter-free mining of large time-evolving graphs. In: Tantipathananandh C, Berger-Wolf T, Kempe D (eds) Proceedings of the 13th ACM SIGKDD Int\u2019l Conference on knowledge discovery and data mining. ACM, San Jose, pp 687\u2013696. https:\/\/doi.org\/10.1145\/1281192.1281266","DOI":"10.1145\/1281192.1281266"},{"issue":"1","key":"2710_CR7","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1108\/IJICC-07-2023-0188","volume":"17","author":"W Wang","year":"2024","unstructured":"Wang W, Li Q, Wei W (2024) An adaptive dynamic community detection algorithm based on multi-objective evolutionary clustering. Int J Intell Comput Cybern 17(1):143\u2013160. https:\/\/doi.org\/10.1108\/IJICC-07-2023-0188","journal-title":"Int J Intell Comput Cybern"},{"issue":"2","key":"2710_CR8","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1109\/TCSS.2021.3114419","volume":"9","author":"C He","year":"2022","unstructured":"He C, Fei X, Cheng Q, Li H, Hu Z, Tang Y (2022) A survey of community detection in complex networks using nonnegative matrix factorization. IEEE Trans Comput Soc Syst 9(2):440\u2013457. https:\/\/doi.org\/10.1109\/TCSS.2021.3114419","journal-title":"IEEE Trans Comput Soc Syst"},{"issue":"12","key":"2710_CR9","doi-asserted-by":"publisher","first-page":"2619","DOI":"10.1587\/transinf.2019EDL8046","volume":"E102.D","author":"Y Pan","year":"2019","unstructured":"Pan Y, Hu G, Pan Z (2019) An evolutionary approach based on symmetric nonnegative matrix factorization for community detection in dynamic networks. IEICE Trans Inf Syst E102.D(12):2619\u20132623. https:\/\/doi.org\/10.1587\/transinf.2019EDL8046","journal-title":"IEICE Trans Inf Syst"},{"issue":"8","key":"2710_CR10","doi-asserted-by":"publisher","first-page":"5314","DOI":"10.1002\/cpe.5314","volume":"33","author":"L Wu","year":"2019","unstructured":"Wu L, Zhang O (2019) Dynamic community detection method based on an improved evolutionary matrix. Concurr Comput Pract Exp 33(8):5314. https:\/\/doi.org\/10.1002\/cpe.5314","journal-title":"Concurr Comput Pract Exp"},{"key":"2710_CR11","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1016\/j.ins.2020.04.031","volume":"534","author":"X Ma","year":"2020","unstructured":"Ma X, Zhang B, Ma C, Ma Z (2020) Co-regularized nonnegative matrix factorization for evolving community detection in dynamic networks. Inf Sci 534:231\u2013248. https:\/\/doi.org\/10.1016\/j.ins.2020.04.031","journal-title":"Inf Sci"},{"key":"2710_CR12","doi-asserted-by":"publisher","unstructured":"Gao F, Yuan L, Wang W, et al (2017) Dynamic community detection using nonnegative matrix factorization. In: 2017 International conference on computing intelligence and information system (CIIS). https:\/\/doi.org\/10.1109\/CIIS.2017.56","DOI":"10.1109\/CIIS.2017.56"},{"issue":"4","key":"2710_CR13","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.88.042812","volume":"88","author":"S Mankad","year":"2013","unstructured":"Mankad S, Michailidis G (2013) Structural and functional discovery in dynamic networks with non-negative matrix factorization. Phys Rev E 88(4):042812. https:\/\/doi.org\/10.1103\/PhysRevE.88.042812","journal-title":"Phys Rev E"},{"issue":"1","key":"2710_CR14","doi-asserted-by":"publisher","first-page":"119","DOI":"10.3233\/IDA-184432","volume":"24","author":"S Wang","year":"2020","unstructured":"Wang S et al (2020) Community detection in dynamic networks using constraint non-negative matrix factorization. Intell Data Anal 24(1):119\u2013139. https:\/\/doi.org\/10.3233\/IDA-184432","journal-title":"Intell Data Anal"},{"key":"2710_CR15","doi-asserted-by":"publisher","first-page":"6423","DOI":"10.1109\/ACCESS.2024.3351709","volume":"12","author":"M Ortiz-Bouza","year":"2024","unstructured":"Ortiz-Bouza M, Aviyente S (2024) Community detection in multiplex networks based on orthogonal nonnegative matrix tri-factorization. IEEE Access 12:6423\u20136436. https:\/\/doi.org\/10.1109\/ACCESS.2024.3351709","journal-title":"IEEE Access"},{"issue":"6","key":"2710_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00362-024-01537-1","volume":"65","author":"Z Li","year":"2024","unstructured":"Li Z, Yang O (2024) A semi-orthogonal nonnegative matrix tri-factorization algorithm for overlapping community detection. Stat Pap 65(6):1\u201319. https:\/\/doi.org\/10.1007\/s00362-024-01537-1","journal-title":"Stat Pap"},{"issue":"2","key":"2710_CR17","doi-asserted-by":"publisher","first-page":"012","DOI":"10.1093\/comnet\/cnae012","volume":"12","author":"J Zhang","year":"2024","unstructured":"Zhang J, Wang F, Zhou J (2024) Community detection based on nonnegative matrix tri-factorization for multiplex social networks. J Complex Netw 12(2):012. https:\/\/doi.org\/10.1093\/comnet\/cnae012","journal-title":"J Complex Netw"},{"key":"2710_CR18","doi-asserted-by":"publisher","unstructured":"Chakrabarti D, Kumar R, Tomkins A (2006) Evolutionary clustering. In: Proceedings of the twelfth ACM SIGKDD international conference on knowledge discovery and data mining. ACM, Philadelphia, PA, USA, pp 554\u2013560. https:\/\/doi.org\/10.1145\/1150402.1150467","DOI":"10.1145\/1150402.1150467"},{"issue":"4","key":"2710_CR19","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1145\/1631162.1631165","volume":"3","author":"Y Chi","year":"2009","unstructured":"Chi Y, Song X, Zhou D, Hino K, Tseng BL (2009) On evolutionary spectral clustering. ACM Trans Knowl Discov Data 3(4):17\u201311730. https:\/\/doi.org\/10.1145\/1631162.1631165","journal-title":"ACM Trans Knowl Discov Data"},{"issue":"8","key":"2710_CR20","doi-asserted-by":"publisher","first-page":"1838","DOI":"10.1109\/TKDE.2013.131","volume":"26","author":"F Folino","year":"2014","unstructured":"Folino F, Pizzuti C (2014) An evolutionary multiobjective approach for community discovery in dynamic networks. IEEE Trans Knowl Data Eng 26(8):1838\u20131852. https:\/\/doi.org\/10.1109\/TKDE.2013.131","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"3","key":"2710_CR21","doi-asserted-by":"publisher","first-page":"1206","DOI":"10.1109\/TKDE.2020.2997043","volume":"34","author":"Z Wang","year":"2022","unstructured":"Wang Z et al (2022) Evolutionary Markov dynamics for network community detection. IEEE Trans Knowl Data Eng 34(3):1206\u20131220. https:\/\/doi.org\/10.1109\/TKDE.2020.2997043","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2710_CR22","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1016\/j.inffus.2021.10.002","volume":"79","author":"W Li","year":"2022","unstructured":"Li W et al (2022) Multi-objective optimization algorithm based on characteristics fusion of dynamic social networks for community discovery. Inf Fusion 79:110\u2013123. https:\/\/doi.org\/10.1016\/j.inffus.2021.10.002","journal-title":"Inf Fusion"},{"issue":"12","key":"2710_CR23","doi-asserted-by":"publisher","first-page":"9665","DOI":"10.1007\/s10462-022-10383-2","volume":"56","author":"AD Abbood","year":"2023","unstructured":"Abbood AD, Attea BA, Hasan AA, Everson RM, Pizzuti C (2023) Community detection model for dynamic networks based on hidden Markov model and evolutionary algorithm. Artif Intell Rev 56(12):9665\u20139697. https:\/\/doi.org\/10.1007\/s10462-022-10383-2","journal-title":"Artif Intell Rev"},{"issue":"5","key":"2710_CR24","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1109\/TKDE.2017.2657752","volume":"29","author":"XK Ma","year":"2017","unstructured":"Ma XK et al (2017) Evolutionary nonnegative matrix factorization algorithms for community detection in dynamic networks. IEEE Trans Knowl Data Eng 29(5):1045\u20131058. https:\/\/doi.org\/10.1109\/TKDE.2017.2657752","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"1","key":"2710_CR25","doi-asserted-by":"publisher","first-page":"119","DOI":"10.3233\/IDA-184432","volume":"24","author":"S Wang","year":"2020","unstructured":"Wang S et al (2020) Community detection in dynamic networks using constraint non-negative matrix factorization. Intell Data Anal 24(1):119\u2013139. https:\/\/doi.org\/10.3233\/IDA-184432","journal-title":"Intell Data Anal"},{"key":"2710_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.106961","volume":"221","author":"D Li","year":"2021","unstructured":"Li D, Zhong O (2021) Detecting dynamic community by fusing network embedding and nonnegative matrix factorization. Knowl-Based Syst 221:106961. https:\/\/doi.org\/10.1016\/j.knosys.2021.106961","journal-title":"Knowl-Based Syst"},{"key":"2710_CR27","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.neucom.2021.01.004","volume":"435","author":"D Li","year":"2021","unstructured":"Li D, Lin O (2021) Identification of dynamic community in temporal network via joint learning graph representation and nonnegative matrix factorization. Neurocomputing 435:77\u201390. https:\/\/doi.org\/10.1016\/j.neucom.2021.01.004","journal-title":"Neurocomputing"},{"issue":"1","key":"2710_CR28","doi-asserted-by":"publisher","first-page":"23741","DOI":"10.1038\/s41598-024-74361-0","volume":"14","author":"Y Pan","year":"2024","unstructured":"Pan Y, Liu X, Yao F et al (2024) Identification of dynamic networks community by fusing deep learning and evolutionary clustering. Sci Rep 14(1):23741. https:\/\/doi.org\/10.1038\/s41598-024-74361-0","journal-title":"Sci Rep"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-026-02710-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10115-026-02710-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-026-02710-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T13:16:03Z","timestamp":1772370963000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10115-026-02710-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,1]]},"references-count":28,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["2710"],"URL":"https:\/\/doi.org\/10.1007\/s10115-026-02710-8","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,1]]},"assertion":[{"value":"22 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 December 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 February 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 March 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"87"}}