SFB-GNN: Spectral Fourier–Bayesian Framework forDynamic Community Detection in Graph Neural Networks

SFB-GNN: Spectral Fourier–Bayesian Framework forDynamic Community Detection in Graph Neural 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 Article SFB-GNN: Spectral Fourier–Bayesian Framework forDynamic Community Detection in Graph Neural Networks Yanfei Ma, Daozheng Qu, Yibo Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9270380/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 16 You are reading this latest preprint version Abstract Community discovery in attributed graphs is acutely responsive to slight structural and feature alterations, frequently resultingin sudden assignment modifications despite strong clustering accuracy in pristine conditions. This deceptive instabilitycompromises the dependability of graph learning models in practical situations, when noise, temporal changes, and structuralambiguity are inevitable. This study introduces SFB-GNN, a Spectral Fourier–Bayesian Graph Neural Network that amalgamatesspectral stability regulation, Bayesian uncertainty modeling, and community-aware message propagation within a cohesiveframework. The model restricts the spectrum response of graph propagation operators using polynomial Fourier filtering, whilevariational Bayesian inference is utilized to measure epistemic uncertainty. Comprehensive experiments on various real-worldgraphs, ranging from weak to strong homophily, sparse to dense connectivity, and small to large scales, reveal that SFB-GNNconsistently mitigates assignment drift amid both structural and feature perturbations while maintaining competitive clusteringaccuracy. The sensitivity analysis conducted under regulated spectral gap conditions confirms that the proposed frameworkattains significant stability improvements without causing representation collapse or excessive smoothing. These findingsconfirm stability-aware community detection as a systematic extension of graph representation learning, integrating spectralgraph theory, probabilistic inference, and resilient graph modeling. Physical sciences/Mathematics and computing Physical sciences/Physics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 15 May, 2026 Reviews received at journal 14 May, 2026 Reviews received at journal 07 May, 2026 Reviews received at journal 05 May, 2026 Reviews received at journal 04 May, 2026 Reviewers agreed at journal 03 May, 2026 Reviewers agreed at journal 02 May, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviewers invited by journal 30 Apr, 2026 Editor invited by journal 06 Apr, 2026 Editor assigned by journal 01 Apr, 2026 Submission checks completed at journal 01 Apr, 2026 First submitted to journal 30 Mar, 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. 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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-9270380","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":636369841,"identity":"b0b20ae1-5a55-4bda-9d11-b83872edb88c","order_by":0,"name":"Yanfei Ma","email":"","orcid":"","institution":"Fairleigh Dickinson University","correspondingAuthor":false,"prefix":"","firstName":"Yanfei","middleName":"","lastName":"Ma","suffix":""},{"id":636369842,"identity":"7d1dbca4-f7e2-490f-b07f-649a8ed9c8b7","order_by":1,"name":"Daozheng Qu","email":"","orcid":"","institution":"Fairleigh Dickinson University","correspondingAuthor":false,"prefix":"","firstName":"Daozheng","middleName":"","lastName":"Qu","suffix":""},{"id":636369843,"identity":"55f2be75-5c04-499a-a8cb-ec705779cc16","order_by":2,"name":"Yibo Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYNACgwMM/Mwka5FsJtGaAyBdxJp//Ozh1zwFd+SMjzMffPCBwSZf3oGQljN5adY8Bs+MzQ6zJRvOYEiz3EjIOrMDOWbGPAaHE7cd5jGT5mE4bGDYQEjL+TcQLZub+b///kOUlhs5xo9BWjYw87ABw/mwgTwBHQz2N96YMc4B+kXiMJuxZI9BmoEBIS2S/TnGH978uSPH33/44YcfFTYG8oQcBgRsUjxwtgEDURHE/PEHMpcYW0bBKBgFo2BkAQDpokDR+nWI2AAAAABJRU5ErkJggg==","orcid":"","institution":"University of California, San Diego","correspondingAuthor":true,"prefix":"","firstName":"Yibo","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-03-30 17:25:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9270380/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9270380/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108807546,"identity":"5820428e-46ec-4608-b503-9c358cdca187","added_by":"auto","created_at":"2026-05-08 15:30:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1560794,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9270380/v1_covered_6e2383cc-8fb9-465f-98f0-427bac758cf1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eSFB-GNN: Spectral Fourier–Bayesian Framework forDynamic Community Detection in Graph Neural 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":"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":"","lastPublishedDoi":"10.21203/rs.3.rs-9270380/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9270380/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Community discovery in attributed graphs is acutely responsive to slight structural and feature alterations, frequently resultingin sudden assignment modifications despite strong clustering accuracy in pristine conditions. 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