Weighted Link Prediction Improvement using Community Detections Algorithms

Weighted Link Prediction Improvement using Community Detections Algorithms | 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 Weighted Link Prediction Improvement using Community Detections Algorithms Zabihullah Burhani, Sadegh Sulaimany, Abolfazl Dibaji This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4901675/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 Link prediction, which aims to estimate missing or future connections in networks, is an important problem with a wide range of applications. Traditional similarity-based link prediction methods exploit local structural features but fail to capture community structures. This paper proposes a weighted link prediction method that incorporates community detection algorithms for computing the proposed methods. Four real-world weighted networks from different domains are analyzed using three established community detection algorithms - Louvain, Girvan-Newman, and ALPA. The identified community structures are then utilized to augment five traditional weighted link prediction methods - WCN, WPA, WAA, WJC, and WRA. Experimental results on the four networks show that the proposed community-informed link prediction approach significantly outperforms the baseline methods, achieving improvements in AUC ranging from 0.32–13.62%. Further analysis indicates that the performance boost depends on the network topology, community structure, and properties of different prediction algorithms. This work demonstrates the importance of leveraging global network structures beyond local features for more accurate link prediction, especially in sparse and scale-free networks. The proposed methods can help advance and apply link prediction across complex networked systems. Weighted link prediction Community Detection combined methods unsupervised. 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-4901675","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":351734220,"identity":"35b765ce-bbc2-4b3a-98bc-98371757bf8a","order_by":0,"name":"Zabihullah Burhani","email":"data:image/png;base64,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","orcid":"","institution":"Takhar University","correspondingAuthor":true,"prefix":"","firstName":"Zabihullah","middleName":"","lastName":"Burhani","suffix":""},{"id":351734221,"identity":"3d155f41-c853-428d-92a9-b6822ffe0c84","order_by":1,"name":"Sadegh Sulaimany","email":"","orcid":"","institution":"University of Kurdistan","correspondingAuthor":false,"prefix":"","firstName":"Sadegh","middleName":"","lastName":"Sulaimany","suffix":""},{"id":351734222,"identity":"a9abf96c-d608-4573-9190-e1c88f5fa25b","order_by":2,"name":"Abolfazl Dibaji","email":"","orcid":"","institution":"University of Kurdistan","correspondingAuthor":false,"prefix":"","firstName":"Abolfazl","middleName":"","lastName":"Dibaji","suffix":""}],"badges":[],"createdAt":"2024-08-12 15:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4901675/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4901675/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66783665,"identity":"927df2a9-355e-4f3d-a4b2-cb88b4788279","added_by":"auto","created_at":"2024-10-16 12:31:55","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":933383,"visible":true,"origin":"","legend":"","description":"","filename":"WeightedLinkPredictionImprovementusingCommunityDetectionsV2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4901675/v1_covered_a973edec-39e7-4dc9-b985-2b348440dae4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Weighted Link Prediction Improvement using Community Detections Algorithms","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":"Weighted link prediction, Community Detection, combined methods, unsupervised.","lastPublishedDoi":"10.21203/rs.3.rs-4901675/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4901675/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLink prediction, which aims to estimate missing or future connections in networks, is an important problem with a wide range of applications. Traditional similarity-based link prediction methods exploit local structural features but fail to capture community structures. This paper proposes a weighted link prediction method that incorporates community detection algorithms for computing the proposed methods. Four real-world weighted networks from different domains are analyzed using three established community detection algorithms - Louvain, Girvan-Newman, and ALPA. The identified community structures are then utilized to augment five traditional weighted link prediction methods - WCN, WPA, WAA, WJC, and WRA. Experimental results on the four networks show that the proposed community-informed link prediction approach significantly outperforms the baseline methods, achieving improvements in AUC ranging from 0.32\u0026ndash;13.62%. Further analysis indicates that the performance boost depends on the network topology, community structure, and properties of different prediction algorithms. This work demonstrates the importance of leveraging global network structures beyond local features for more accurate link prediction, especially in sparse and scale-free networks. 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