Key node recognition based on multidimensional feature extraction

Key node recognition based on multidimensional feature extraction | 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 Key node recognition based on multidimensional feature extraction YunLong Peng, Han Li, Xiang Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6846208/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 remains a prominent research focus within the domain of complex networks. Numerous researchers have proposed diverse methodologies predicated on structural characteristics, including network connectivity, distance metrics, eigenvalues and community structures. However, the identification of key nodes through the extraction of compressed network features remains a less explored avenue. To address this gap, this paper proposes a key node identification method predicated on the extraction of multi-dimensional network features. This approach involves extracting multi-dimensional features, specifically encompassing network visibility, phase space characteristics and state transition dynamics. Acknowledging the inherent nonlinear relationships among these features, a Random Forest algorithm is employed to model these interdependencies and perform the ultimate key node identification. The effectiveness of the proposed method was validated using the Susceptible-Infected-Recovered (SIR) model, with Kendall’s τ and Pearson correlation coefficients employed to assess ranking correlation. Preliminary investigations also assessed the impact of varying quantities of extracted features on the experimental results. The proposed method was evaluated on ten real-world network datasets and benchmarked against ten typical existing algorithms. Experimental results demonstrate that the proposed methodology offers significant advantages over extant approaches. This application of multidimensional feature extraction for identifying key nodes offers a novel perspective and a valuable analytical tool, thereby contributing to the advancement of research in this domain. Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Information technology Complex network Key nodes identification Graph embedding Random Forest Full Text Additional Declarations No competing interests reported. Supplementary Files sample.bib 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-6846208","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":475782774,"identity":"b146cff1-87d9-473f-a699-928774394b76","order_by":0,"name":"YunLong Peng","email":"data:image/png;base64,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","orcid":"","institution":"Liaoning University of Technology","correspondingAuthor":true,"prefix":"","firstName":"YunLong","middleName":"","lastName":"Peng","suffix":""},{"id":475782775,"identity":"1f34c4cd-e9f9-4081-bd04-9a270024aee1","order_by":1,"name":"Han Li","email":"","orcid":"","institution":"Liaoning University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Han","middleName":"","lastName":"Li","suffix":""},{"id":475782776,"identity":"c44915b0-5415-402a-b100-aeab514c98cc","order_by":2,"name":"Xiang Li","email":"","orcid":"","institution":"Liaoning University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-06-08 08:08:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6846208/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6846208/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99788831,"identity":"17c82906-b627-4f78-8d33-73fa5dec742c","added_by":"auto","created_at":"2026-01-08 12:47:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3358214,"visible":true,"origin":"","legend":"","description":"","filename":"paperofSR.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6846208/v1_covered_5e6d36f3-9cad-433d-8b9f-e7fe74d82e42.pdf"},{"id":85511388,"identity":"7c122390-fa8e-428c-8ba0-bdead4c5d8be","added_by":"auto","created_at":"2025-06-26 16:40:23","extension":"bib","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":10607,"visible":true,"origin":"","legend":"","description":"","filename":"sample.bib","url":"https://assets-eu.researchsquare.com/files/rs-6846208/v1/090ffb15905347aa55bcf96a.bib"}],"financialInterests":"No competing interests reported.","formattedTitle":"Key node recognition based on multidimensional feature extraction","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":"Complex network, Key nodes identification, Graph embedding, Random Forest","lastPublishedDoi":"10.21203/rs.3.rs-6846208/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6846208/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Key node identification remains a prominent research focus within the domain of complex networks. 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