Key node identification method based on hybrid centrality and graph neural network

Key node identification method based on hybrid centrality and graph neural network | 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 Research Article Key node identification method based on hybrid centrality and graph neural network Feipeng Guo, Ben Cao, Jianwei Sun, Shaobo Ji, Wei Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8521739/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Key node identification has become an important part of complex network research. However, existing key node identification methods suffer from low computational efficiency and insufficient robustness. This paper proposes to obtain a mixed centrality by combining the local and global influences of nodes, and after combining it with the graph convolutional neural network, a key node identification model is proposed. The model constructs multi-scale centrality features using mixed node centrality and builds two structural channel sets to embed neighborhood topology information. Subsequently, an attention mechanism is introduced to assign weights to different channels automatically. Finally, feature aggregation and regression prediction of nodes are carried out by the graph neural network. Experimental results on multiple real social networks and synthetic scale-free networks show that NLGCN outperforms classical centrality methods and existing graph learning models in terms of propagation ability, node ranking consistency, robustness, and generalization performance. Social networks key nodes identification graph neural networks hybrid centrality attention mechanism Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8521739","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":581446225,"identity":"b7706c7f-417a-4900-924f-10c7e3ea11d5","order_by":0,"name":"Feipeng Guo","email":"","orcid":"","institution":"Zhejiang Gongshang University","correspondingAuthor":false,"prefix":"","firstName":"Feipeng","middleName":"","lastName":"Guo","suffix":""},{"id":581446226,"identity":"dd349865-9d36-43a0-a63e-70ec90310cdb","order_by":1,"name":"Ben Cao","email":"","orcid":"","institution":"Zhejiang Gongshang University","correspondingAuthor":false,"prefix":"","firstName":"Ben","middleName":"","lastName":"Cao","suffix":""},{"id":581446227,"identity":"63a1c232-cf7e-47ce-bcc2-304271469285","order_by":2,"name":"Jianwei Sun","email":"","orcid":"","institution":"Zhejiang Gongshang University","correspondingAuthor":false,"prefix":"","firstName":"Jianwei","middleName":"","lastName":"Sun","suffix":""},{"id":581446228,"identity":"b362a47a-1d55-4a12-9626-b04a2c73eb5b","order_by":3,"name":"Shaobo Ji","email":"","orcid":"","institution":"Carleton University","correspondingAuthor":false,"prefix":"","firstName":"Shaobo","middleName":"","lastName":"Ji","suffix":""},{"id":581446229,"identity":"4ecc34f6-ba21-41ad-a4e6-d3bd4051f3b9","order_by":4,"name":"Wei Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYDACCQgpx8BwsAHEYmwgVosxWMsBErQwJIJVEqVFfnbzMWmeCov0+Y2HGx9/YLCR3XCA+dkDfFoY5xxLk+Y5I5Hb2HCw2eAAQ5rxhgNs5gb4tDBL5JhJ57ZJ5DYzHGyTOMBwOHHDAR42CXxa2MBa/kmkszEcbP9xgOE/YS08YC0NEgk8QFuA3j9AWIuERFqy9Z9jEoYzGA42S5wxSDaeeZjNDK8W+RnJB2/OqKmTl59x/OGHigo72b7jzc/wakGy7wCQAAUVM3HqgYC/gWilo2AUjIJRMMIAAA0tSJNE/tfbAAAAAElFTkSuQmCC","orcid":"","institution":"Zhejiang Institute of Economics and Trade","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2026-01-05 13:13:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8521739/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8521739/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108183101,"identity":"e27c6cff-d08b-445a-aea6-0f4126352b02","added_by":"auto","created_at":"2026-04-30 08:59:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1015429,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8521739/v1_covered_67988d89-4047-46cb-850e-088f2b5859d4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Key node identification method based on hybrid centrality and graph neural network","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Social networks, key nodes identification, graph neural networks, hybrid centrality, attention mechanism","lastPublishedDoi":"10.21203/rs.3.rs-8521739/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8521739/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eKey node identification has become an important part of complex network research. 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