Identification of dynamic networks community by fusing deep learning and evolutionary clustering

Identification of dynamic networks community by fusing deep learning and evolutionary clustering | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Identification of dynamic networks community by fusing deep learning and evolutionary clustering Yu Pan, Feng Yao, Xin Liu, Lei Zhang, Wei Li, Pei Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4236904/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Oct, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Community detection is a critical component of network analysis and a hot topic in social computing. Detecting community structure in dynamic networks has important theoretical and practical implications for understanding the intrinsic function of networks and predicting network behavior. However, the majority of existing dynamic community detection methods adopt shallow models and merely excavate the complex non-linear structure, which tends to generate undesirable community structure. In order to obtain the accurate and robust community structure in dynamic networks, we are inspired by network representation learning and utilize the deep learning to detect evolving communities in dynamic networks. In this paper, we propose a novel dynamic community detection method by fusing Deep Learning and Evolutionary Clustering(DLEC). This work attempts to combine deep learning and evolutionary clustering into a unified framework. First, we propose a matrix construction strategy to fully reveal the inherent community structures via the underlying community memberships. Then, we develop a novel multi-layer deep autoencoder framework that consists of multiple non-linear functions to extract the latent deep representation of the dynamic network. Based on the evolutionary clustering framework, a graph regularization term is introduced to ensure the smoothness of the community evolution. Finally, we employ the K-means clustering algorithm on the low-dimensional network space to obtain the community structure. Extensive experimental results on synthetic and real-world networks show that the proposed DLEC algorithm can effectively detect high-quality communities in the dynamic networks. Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Information technology Physical sciences/Mathematics and computing/Scientific data Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Oct, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 17 Jun, 2024 Reviews received at journal 12 Jun, 2024 Reviews received at journal 05 Jun, 2024 Reviewers agreed at journal 18 May, 2024 Reviewers agreed at journal 17 May, 2024 Reviewers invited by journal 17 May, 2024 Editor assigned by journal 07 May, 2024 Editor invited by journal 30 Apr, 2024 Submission checks completed at journal 30 Apr, 2024 First submitted to journal 08 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4236904","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":299369178,"identity":"0a81c838-c0f5-492a-88f4-4af51dd51eef","order_by":0,"name":"Yu Pan","email":"","orcid":"","institution":"National University of Defense Technology","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Pan","suffix":""},{"id":299369184,"identity":"7c1aa236-418f-4fd2-a7d5-0c7fa57182c2","order_by":1,"name":"Feng Yao","email":"","orcid":"","institution":"National University of Defense Technology","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Yao","suffix":""},{"id":299369190,"identity":"e26eb682-b6c4-4d46-8c67-5962f8541225","order_by":2,"name":"Xin Liu","email":"","orcid":"","institution":"Army Engineering University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Liu","suffix":""},{"id":299369196,"identity":"120cf2ac-01c0-4817-8e46-7f1a3888894a","order_by":3,"name":"Lei Zhang","email":"","orcid":"","institution":"Academy of Military Science","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Zhang","suffix":""},{"id":299369202,"identity":"434d24c9-9636-4c2f-99a1-e36b72b2103e","order_by":4,"name":"Wei Li","email":"","orcid":"","institution":"Army Engineering University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Li","suffix":""},{"id":299369208,"identity":"51c248cd-1a70-49ea-94ee-8c6f9f59c37d","order_by":5,"name":"Pei Wang","email":"data:image/png;base64,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","orcid":"","institution":"National University of Defense Technology","correspondingAuthor":true,"prefix":"","firstName":"Pei","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-04-08 13:43:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4236904/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4236904/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-74361-0","type":"published","date":"2024-10-10T15:58:09+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":66597298,"identity":"b38065f3-30c2-4ce7-872d-7fa68bfebdd4","added_by":"auto","created_at":"2024-10-14 16:09:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1551531,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4236904/v1_covered_6e6fe262-ed7d-4306-a75a-5df1979329d8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of dynamic networks community by fusing deep learning and evolutionary clustering","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4236904/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4236904/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Community detection is a critical component of network analysis and a hot topic in social computing. 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