HA: An influential node identification method based on hub-triggered neighborhood decomposition and asymmetric order-by-order recurrence model

HA: An influential node identification method based on hub-triggered neighborhood decomposition and asymmetric order-by-order recurrence model | 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 HA: An influential node identification method based on hub-triggered neighborhood decomposition and asymmetric order-by-order recurrence model Min Zhao, JunHan Ye, JiaYun li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4546606/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 In recent years, the proliferation of major power network security incidents triggered by malicious attacks has drawn widespread attention to influential nodes in power networks. Among the existing influential node identification algorithms, the global algorithms are too complex and not suitable for large-scale networks. The local algorithms are too simply to ignore the differentiated features of the same node in different information dissemination processes. To overcome these shortcomings, this paper proposes a novel influential node identification method based on hub-triggered neighborhood decomposition and asymmetric order-by-order recurrence model. The concepts of network directionalization strategy and hub-triggered neighborhood decomposition are introduced to distinguish the functional differences between nodes in the virus-spreading process. Moreover, this paper proposes the concepts of infected potential and infecting potential, and constructs a calculation model with asymmetric characteristics based on the order-by-order recurrence method to make full use of the information in the connection structure of the adjacent neighborhood. Finally, the influence of the hub node is evaluated by integrating the infected potential and infecting potential of neighbors of multiple orders. Experimental results show that the algorithm proposed in this paper outperforms the other six algorithms in terms of SIR correlation coefficient, imprecision function and algorithm resolution. 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. 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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-4546606","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":321598451,"identity":"f86f47b8-f8c5-49f7-898e-b10283b4556e","order_by":0,"name":"Min Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYDACCRBhwCBnAOEyE6/FGKiFsYEELQwMiRuI1iI/u/nhoxsFd9K3S+Qef8BQYZ3YwH72AF4tjHOOGRvnGDzL3TkjL7GB4Ux6YgNPXgJeLcwSCWbSOQaHczfcyDFsYGw7nNggwWOAVwubRPo3kJZ0A7CWf0Ro4ZHIAduSANHSQIQWCYmcYqBfDhvu7HmXOCPhWLpxG08Ofi3yM9I3Ps75c1jenD33wIcPNday/exn8GtBdiMDQwLId8Sqh2gZBaNgFIyCUYANAACAZEPbHB8F4AAAAABJRU5ErkJggg==","orcid":"","institution":"Beijing University of Posts and Telecommunications","correspondingAuthor":true,"prefix":"","firstName":"Min","middleName":"","lastName":"Zhao","suffix":""},{"id":321598452,"identity":"0ae4ecb6-d9f0-408a-97ca-f9ebf6118bc6","order_by":1,"name":"JunHan Ye","email":"","orcid":"","institution":"Beijing University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"JunHan","middleName":"","lastName":"Ye","suffix":""},{"id":321598453,"identity":"1410c454-8bc4-45ae-88af-adc880136f35","order_by":2,"name":"JiaYun li","email":"","orcid":"","institution":"Beijing University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"JiaYun","middleName":"","lastName":"li","suffix":""}],"badges":[],"createdAt":"2024-06-07 14:06:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4546606/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4546606/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96603841,"identity":"7f3b8e28-cb74-4704-ab92-2e83f23f26ba","added_by":"auto","created_at":"2025-11-24 09:11:44","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6556034,"visible":true,"origin":"","legend":"","description":"","filename":"TemplateforsubmissionstoScientificReports.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4546606/v1_covered_90bc7a03-efa9-4092-976c-b45ce3d8d7ff.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"HA: An influential node identification method based on hub-triggered neighborhood decomposition and asymmetric order-by-order recurrence model","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":"","lastPublishedDoi":"10.21203/rs.3.rs-4546606/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4546606/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In recent years, the proliferation of major power network security incidents triggered by malicious attacks has drawn widespread attention to influential nodes in power networks. Among the existing influential node identification algorithms, the global algorithms are too complex and not suitable for large-scale networks. The local algorithms are too simply to ignore the differentiated features of the same node in different information dissemination processes. To overcome these shortcomings, this paper proposes a novel influential node identification method based on hub-triggered neighborhood decomposition and asymmetric order-by-order recurrence model. The concepts of network directionalization strategy and hub-triggered neighborhood decomposition are introduced to distinguish the functional differences between nodes in the virus-spreading process. Moreover, this paper proposes the concepts of infected potential and infecting potential, and constructs a calculation model with asymmetric characteristics based on the order-by-order recurrence method to make full use of the information in the connection structure of the adjacent neighborhood. Finally, the influence of the hub node is evaluated by integrating the infected potential and infecting potential of neighbors of multiple orders. 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