Community detection and Higher-order Link Prediction

Community detection and Higher-order Link Prediction | 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PDF Research Article Community detection and Higher-order Link Prediction Jelena Losic This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3998813/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 Community detection algorithms utilize graphs as input data, representing networks that are often incomplete. Many networks, whether obtained through collection or inference, suffer from missing edges, either inherent to the dataset or due to sampling limitations. Networks evolve over time, and it is imperative to predict future links. An excellent example is the spread of epidemics, where such predictive knowledge could potentially save lives. The standard approach involves examining the dyadic nature of links, as it is the perspective offered by graph frameworks, and attempting to anticipate future edges. However, being part of a large system entails multiple interactions that the link paradigm does not clearly elucidate. The idea presented in this paper is to employ higher-order link prediction to achieve robust results regarding the community detection problem. Finally, we evaluate our method on a series of ground truth networks and artificial networks. Examples from the LFR framework demonstrate that our method improves the community detection problem on incomplete networks. simplex community detection complex networks link prediction 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. 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-3998813","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276732654,"identity":"4c12cf46-0876-4211-8c13-c7afa64cd405","order_by":0,"name":"Jelena Losic","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYBACxnYgkcBgw8MP4iUUEKOlGawlTUayAcQwIMYaZjB52MbgAIgmRgtzM/PhDw9qDvMYn1+d+OGBAYM8v9gBQg5jS5NIOJbOY3bj7WYJoMMMZ85OIKSFx4whscEaqOXsBpCWBIPbhLUYf0hsYOYxnnF28w9itRhIJDY48xjw924j1hawX9J4JG7wbrNIMJAg7BfD9ubDH3/U2Njz95/dfPNHhY08vzQhLQ0wlgRYpQR+5SAgD2fxHyCsehSMglEwCkYmAADrl0BxsnI9tAAAAABJRU5ErkJggg==","orcid":"","institution":"University of Belgrade","correspondingAuthor":true,"prefix":"","firstName":"Jelena","middleName":"","lastName":"Losic","suffix":""}],"badges":[],"createdAt":"2024-02-29 07:02:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3998813/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3998813/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93592579,"identity":"e4cf1e1d-80fc-4ce9-8413-13a3ac79010f","added_by":"auto","created_at":"2025-10-15 13:02:10","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":174211,"visible":true,"origin":"","legend":"","description":"","filename":"HigherorderLPCD.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3998813/v1_covered_c92d2c7d-67b8-473a-a515-c41bd6a7a505.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Community detection and Higher-order Link Prediction","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":"simplex, community detection, complex networks, link prediction","lastPublishedDoi":"10.21203/rs.3.rs-3998813/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3998813/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Community detection algorithms utilize graphs as input data, representing networks that are often incomplete. 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