Sparsity-Aware Social Recommendation via Information Graph Bottleneck

Sparsity-Aware Social Recommendation via Information Graph Bottleneck | 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 Sparsity-Aware Social Recommendation via Information Graph Bottleneck Jialei Zhu, Yunlong Song, Hongyun Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9217535/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract With the widespread adoption of online social networks, social recommender systems have become a key paradigm for mitigating data sparsity and cold-start issues in traditional collaborative filtering. However, existing graph neural network (GNN) based recommendation models face two fundamental structural bottlenecks when dealing with real-world social data: social noise and over-smoothing. Invalid or weak social connections dilute preference signals, while representation homogenization induced by deep graph convolution undermines personalized recommendation. To address these challenges, this report proposes a new framework named SRIGB (Social Recommendation via Information Graph Bottleneck). SRIGB introduces the Information Bottleneck principle as a principled denoising mechanism by maximizing the mutual information between the denoised graph and interaction labels while minimizing redundant information with respect to the original noisy graph, thereby extracting task-relevant social structure more precisely. In addition, SRIGB incorporates a contrastive learning paradigm to counteract the smoothing effect of graph convolution, enabling the model to preserve high-order social influence while maintaining discriminative and independent user representations. This report presents the theoretical foundations, model architecture, and experimental design of SRIGB, aiming to provide both theoretical support and a practical technical pathway for building the next generation of robust social recommender systems. Social Recommendation Graph Neural Networks Social Noise Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 15 May, 2026 Reviewers agreed at journal 11 May, 2026 Reviewers agreed at journal 08 May, 2026 Reviews received at journal 05 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviewers invited by journal 05 May, 2026 Editor assigned by journal 14 Apr, 2026 Submission checks completed at journal 05 Apr, 2026 First submitted to journal 05 Apr, 2026 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-9217535","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":638610829,"identity":"a3829420-6602-485f-af5e-4e15d2027edf","order_by":0,"name":"Jialei Zhu","email":"","orcid":"","institution":"Tongji University","correspondingAuthor":false,"prefix":"","firstName":"Jialei","middleName":"","lastName":"Zhu","suffix":""},{"id":638610830,"identity":"e5fa178f-957b-4a13-9ffa-d9964c19f1ed","order_by":1,"name":"Yunlong Song","email":"","orcid":"","institution":"Tongji University","correspondingAuthor":false,"prefix":"","firstName":"Yunlong","middleName":"","lastName":"Song","suffix":""},{"id":638610833,"identity":"9d84ab14-f8bc-4601-a640-b0b234665381","order_by":2,"name":"Hongyun Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFklEQVRIiWNgGAWjYDACCSBmbIByPoDJg0AuG5FaGGckkKqFmScBJoxHi/zs5mcPv+6wyZOPyDF7bPsDyHA83MDwoewwA//sBqxaGOccMzeWPZNWbHgjx9w4JwHIOHCwgXHGucMMEncOYNXCLJFgJi3Zdjhx44wcM+mcBCCj4WADM2/bYQYDiQSsWtgk0r8htFjAtPzFo4VHIsdM8iNQy3wgQ5oBqGU+MMSYGfFokZDIKZNmbEtL3MDzrEyyJw3IAGo52HMunUfiBnYt8jPSt0n+bLNJnN+evE3ihw2QMeP4wwc/yqzl+Gdg1wIOAh4gYXABqsDgxgGGAyAn41QPBIw/QNb1H4Da29+AT/EoGAWjYBSMQAAAupdmTts6Z+kAAAAASUVORK5CYII=","orcid":"","institution":"Tongji University","correspondingAuthor":true,"prefix":"","firstName":"Hongyun","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2026-03-25 03:23:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9217535/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9217535/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109249685,"identity":"45e4e3c0-bcd7-4d8e-9faf-60fbdc52c91c","added_by":"auto","created_at":"2026-05-14 08:59:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1888434,"visible":true,"origin":"","legend":"","description":"","filename":"Springer2026SRIGB20260405.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9217535/v1_covered_448b86e1-c935-405e-8137-a6b7f22f4540.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sparsity-Aware Social Recommendation via Information Graph Bottleneck","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":"discover-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Computing](https://link.springer.com/journal/10791)","snPcode":"10791","submissionUrl":"https://submission.springernature.com/new-submission/10791/3","title":"Discover Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Social Recommendation, Graph Neural Networks, Social Noise","lastPublishedDoi":"10.21203/rs.3.rs-9217535/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9217535/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the widespread adoption of online social networks, social recommender systems have become a key paradigm for mitigating data sparsity and cold-start issues in traditional collaborative filtering. 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