A dynamic propagation-based algorithm for node diffusion capacity evaluation in complex networks

A dynamic propagation-based algorithm for node diffusion capacity evaluation in complex networks | 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 A dynamic propagation-based algorithm for node diffusion capacity evaluation in complex networks Xiaoyu chen, xingbao Gao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7219502/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Accurately evaluating the diffusion capacity of network nodes is crucial for understanding network structure and dynamic information propagation, with applications ranging from information dissemination and epidemic modeling to critical infrastructure protection. However, traditional centrality measures and diffusion metrics based on static network topology fail to capture dynamic propagation processes. To address this problem, we propose a novel dynamic propagation-based algorithm, termed NDC (Node Diffusion Capacity), for evaluating a node's diffusion ability. The proposed algorithm models propagation dynamics through three components-diffusion gain, loss, and stability-and quantifies each node's diffusion capacity by analyzing its propagation distance distribution and computing a Wasserstein distance. We conduct experiments on both weighted and unweighted networks, comparing NDC with 11 benchmark algorithms. Results show that NDC achieves a higher final infection ratio, shorter propagation duration, and greater average infection rate than baseline methods, with statistical significance confirmed by t-tests. Robustness tests under node removal, edge removal, and topological perturbations on Barab{'a}si-Albert and Erd{"o}s-R{'e}nyi networks further demonstrate NDC's stability. Finally, experiments on 17 real-world networks (weighted and unweighted) indicate that NDC outperforms baseline algorithms in path survival rate, infection rate, redundancy, and propagation efficiency. These findings offer a novel perspective for assessing node diffusion capacity in complex networks. Diffusion capacity Complex networks Distance distribution Wasserstein distance Robustness analysis. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 16 Sep, 2025 Reviews received at journal 09 Sep, 2025 Reviews received at journal 04 Sep, 2025 Reviewers agreed at journal 30 Aug, 2025 Reviewers agreed at journal 30 Aug, 2025 Reviewers invited by journal 29 Aug, 2025 Editor assigned by journal 29 Aug, 2025 Submission checks completed at journal 26 Jul, 2025 First submitted to journal 26 Jul, 2025 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-7219502","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":509587979,"identity":"f1f637f3-2a93-4450-b147-6b3c6bb37a80","order_by":0,"name":"Xiaoyu chen","email":"","orcid":"","institution":"Shaanxi Normal University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyu","middleName":"","lastName":"chen","suffix":""},{"id":509587981,"identity":"6d7a1611-0148-4fdf-8fc2-39941b79ea54","order_by":1,"name":"xingbao Gao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYDACZgY2ECUH5xKtxZgELQwQLYkNRGsxOM577MGPitr0+bN7j0kwVFgnNrCfPYBXi2QzX7phz5njuRvunEuTYDiTntjAk5eAVws/M4+ZBG/bsdwNEjlmEoxthxMbJHgM8HsEqEXyb9uxdPkZIC3/iNACskWat60mgeEGSEsDEVokm3nMjWXOHDDccCPH2CLhWLpxG08Ofi0G58+YPXxTUScPdJjhjQ811rL97Gfwa4GCwxAqgQEaTUSAOiLVjYJRMApGwYgEAN7aPY+2whhJAAAAAElFTkSuQmCC","orcid":"","institution":"Shaanxi Normal University","correspondingAuthor":true,"prefix":"","firstName":"xingbao","middleName":"","lastName":"Gao","suffix":""}],"badges":[],"createdAt":"2025-07-26 07:53:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7219502/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7219502/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90669081,"identity":"9e10695a-c270-4bef-96f3-106bd81c7092","added_by":"auto","created_at":"2025-09-05 13:16:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":21809829,"visible":true,"origin":"","legend":"","description":"","filename":"NDC.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7219502/v1_covered_291a5fef-32ca-4175-a519-c5fe8a835095.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eA dynamic propagation-based algorithm for node diffusion capacity evaluation in complex networks \u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"cluster-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Cluster Computing](https://www.springer.com/journal/10586)","snPcode":"10586","submissionUrl":"https://submission.nature.com/new-submission/10586/3","title":"Cluster Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Diffusion capacity, Complex networks, Distance distribution, Wasserstein distance, Robustness analysis.","lastPublishedDoi":"10.21203/rs.3.rs-7219502/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7219502/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Accurately evaluating the diffusion capacity of network nodes is crucial for understanding network structure and dynamic information propagation, with applications ranging from information dissemination and epidemic modeling to critical infrastructure protection. 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