Dynamic Identification of Important Nodes in Complex Networks based on the KPDN-INCC Method

Dynamic Identification of Important Nodes in Complex Networks based on the KPDN-INCC Method | 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 Dynamic Identification of Important Nodes in Complex Networks based on the KPDN-INCC Method Jieyong Zhang, Liang Zhao, Peng Sun, Wei Liang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3740335/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Mar, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Dynamic identification of influential nodes in complex networks is of great significance for practical applications. In real-world scenarios, resources are often limited, making it necessary to evaluate nodes by iteratively assessing the remaining network after removing certain nodes. Therefore, a dynamic identification method for important nodes in complex networks is more suitable for real-world applications. This paper proposes a method that combines both local and global characteristics. For the global features, we introduce an improved k-shell method that integrates the fusion degree, enhancing the resolution of node rankings. For the local features, we introduce the Solton factor and the improved network constraint coefficient (INCC) to enhance the algorithm's understanding of the relationship between neighboring nodes. Through a comparison with existing methods, we find that the proposed KPDN-INCC method complements the KPDN method and accurately identifies important nodes, thus facilitating rapid network disintegration. The experiments on artificial networks further validate the effectiveness of the proposed method in identifying important nodes in small-world networks with a random parameter less than 0.4. Complex networks dynamic attack node importance INCC KPDN Full Text Additional Declarations No competing interests reported. Supplementary Files Appendix1.docx Appendix2CodeandDatasets.zip Cite Share Download PDF Status: Published Journal Publication published 09 Mar, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 05 Feb, 2024 Reviews received at journal 04 Feb, 2024 Reviews received at journal 28 Jan, 2024 Reviewers agreed at journal 22 Jan, 2024 Reviewers agreed at journal 22 Jan, 2024 Reviewers invited by journal 22 Jan, 2024 Editor assigned by journal 20 Dec, 2023 Editor invited by journal 17 Dec, 2023 Submission checks completed at journal 16 Dec, 2023 First submitted to journal 11 Dec, 2023 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-3740335","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":260933960,"identity":"396f3d8d-1119-4e01-b2ba-f0d03d863e40","order_by":0,"name":"Jieyong Zhang","email":"","orcid":"","institution":"Information and Navigation College, Air Force Engineering University.","correspondingAuthor":false,"prefix":"","firstName":"Jieyong","middleName":"","lastName":"Zhang","suffix":""},{"id":260933962,"identity":"13daad0d-e01d-49f6-8244-dd3714d3d5d6","order_by":1,"name":"Liang Zhao","email":"","orcid":"","institution":"No.96872 Troops of PLA","correspondingAuthor":false,"prefix":"","firstName":"Liang","middleName":"","lastName":"Zhao","suffix":""},{"id":260933965,"identity":"c10d1031-721b-46b8-82f0-42d74f51a149","order_by":2,"name":"Peng Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIie3PsWrDMBCAYQmDvCh4lUiJXuGEh3YI9FWkxZObpVAylHIm4Cx+AA+lj5HZQZDJdPZot2sH7w20bqZOtsdC9W0H93MSIZ73R/UtWfMozLK234qVmlHQ0pDkShbO6bK+iTXOS9wamiRZLvKtJdXEvirvjmjzgEtMQSxehKEYdG/NSALNxg4J4xGpAeRBbELC4jgdS0R63dqcc5kVYPRB3FPkbDmWqDLVwxXBwXGo7LOwWE0kpLkkwOHELB5xRgL1h0bzargsAkfxJGK9m/iL2g9X+oev20h1+8/z49NKhbvuffRhPyj7PQVT6xfnWVue53n/1TeR+k85gm//IwAAAABJRU5ErkJggg==","orcid":"","institution":"Information and Navigation College, Air Force Engineering University.","correspondingAuthor":true,"prefix":"","firstName":"Peng","middleName":"","lastName":"Sun","suffix":""},{"id":260933967,"identity":"92598be7-5b40-482f-ac3f-1ef7fce4af1a","order_by":3,"name":"Wei Liang","email":"","orcid":"","institution":"Information and Navigation College, Air Force Engineering University.","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Liang","suffix":""}],"badges":[],"createdAt":"2023-12-11 20:42:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3740335/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3740335/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-56226-8","type":"published","date":"2024-03-09T15:01:01+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":52432218,"identity":"b5788fa7-0e06-4067-822e-27fee5dbcce9","added_by":"auto","created_at":"2024-03-11 15:11:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":978173,"visible":true,"origin":"","legend":"","description":"","filename":"1217.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3740335/v1_covered_5e6cf738-c833-4bdc-8728-720be450580a.pdf"},{"id":48478842,"identity":"a9751ee3-68b6-47c1-8881-83c3ac99717c","added_by":"auto","created_at":"2023-12-19 17:45:25","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":40508,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3740335/v1/5e585f8cdce30d188859e314.docx"},{"id":48478843,"identity":"3f4eb066-0cd0-4498-b6cc-4ed18266724a","added_by":"auto","created_at":"2023-12-19 17:45:25","extension":"zip","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":913316,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix2CodeandDatasets.zip","url":"https://assets-eu.researchsquare.com/files/rs-3740335/v1/49b7bd02ddb0322c08a1f8df.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dynamic Identification of Important Nodes in Complex Networks based on the KPDN-INCC Method","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":"Complex networks, dynamic attack, node importance, INCC, KPDN","lastPublishedDoi":"10.21203/rs.3.rs-3740335/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3740335/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDynamic identification of influential nodes in complex networks is of great significance for practical applications. In real-world scenarios, resources are often limited, making it necessary to evaluate nodes by iteratively assessing the remaining network after removing certain nodes. Therefore, a dynamic identification method for important nodes in complex networks is more suitable for real-world applications. This paper proposes a method that combines both local and global characteristics. For the global features, we introduce an improved k-shell method that integrates the fusion degree, enhancing the resolution of node rankings. For the local features, we introduce the Solton factor and the improved network constraint coefficient (INCC) to enhance the algorithm's understanding of the relationship between neighboring nodes. Through a comparison with existing methods, we find that the proposed KPDN-INCC method complements the KPDN method and accurately identifies important nodes, thus facilitating rapid network disintegration. The experiments on artificial networks further validate the effectiveness of the proposed method in identifying important nodes in small-world networks with a random parameter less than 0.4.\u003c/p\u003e","manuscriptTitle":"Dynamic Identification of Important Nodes in Complex Networks based on the KPDN-INCC Method","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-12-19 17:45:19","doi":"10.21203/rs.3.rs-3740335/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-02-05T06:06:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-02-04T20:34:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-01-28T11:22:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5331aee9-0e57-4383-b227-0aa27848f906","date":"2024-01-22T23:01:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"42d504b6-0658-4fad-a7f8-3eb14e00d619","date":"2024-01-22T21:54:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-22T21:49:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-12-20T11:56:21+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2023-12-17T05:46:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-12-17T03:20:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2023-12-11T19:56:06+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"a2ee19ab-bf37-40f8-996f-da263fa2c1b7","owner":[],"postedDate":"December 19th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-03-11T15:09:05+00:00","versionOfRecord":{"articleIdentity":"rs-3740335","link":"https://doi.org/10.1038/s41598-024-56226-8","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-03-09 15:01:01","publishedOnDateReadable":"March 9th, 2024"},"versionCreatedAt":"2023-12-19 17:45:19","video":"","vorDoi":"10.1038/s41598-024-56226-8","vorDoiUrl":"https://doi.org/10.1038/s41598-024-56226-8","workflowStages":[]},"version":"v1","identity":"rs-3740335","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3740335","identity":"rs-3740335","version":["v1"]},"buildId":"WvIrzKhiLBfengagbw6Ux","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}