Graph Analytics for Blockchain Fraud Detection: A Comprehensive Review and the GraphGuard Framework

Graph Analytics for Blockchain Fraud Detection: A Comprehensive Review and the GraphGuard Framework | 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 Graph Analytics for Blockchain Fraud Detection: A Comprehensive Review and the GraphGuard Framework Mahitha Geddavalasa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9369116/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 The decentralized nature of blockchain ecosystems, while fostering transparency and trustless transactions, has simultaneously facilitated the rise of complex fraudulent activities. From multi-hop phishing and Ponzi schemes to sophisticated money laundering operations, the scale of illicit financial flows in cryptocurrency networks has become a significant challenge for regulatory compliance and network security. Traditional machine learning (ML) methodologies, which rely on handcrafted statistical features and local heuristics, often fail to capture the complex relational and structural dependencies inherent in large-scale transaction graphs. This paper provides a comprehensive review of blockchain fraud detection through the lens of graph analytics, with a specific focus on the evolution toward Graph Neural Networks (GNNs). We identify a critical research gap: the inability of existing models to effectively handle "camouflage" behavior where fraudsters intentionally interact with benign entities to obscure their structural identity. To address this, we propose GraphGuard, a modular framework that integrates spatial-temporal graph modeling with heterophily-aware aggregation strategies. Our methodology evaluates the transition from topological modeling to dynamic sequence analysis, highlighting how GNNs address structural neglect and temporal evolution. Analysis of real-world datasets reveals that spatial-temporal models achieve detection accuracy exceeding 98%, significantly outperforming traditional ML. The contributions of this work include a systematic categorization of contemporary research, the design of a novel detection architecture, and a critical analysis of adversarial evasion and scalability. This synthesis serves as a roadmap for developing high-fidelity security solutions in dynamic blockchain environments, providing both theoretical foundations and practical insights for researchers and practitioners working to secure the over $82 billion in cryptocurrencies currently at risk. Blockchain Security Fraud Detection Graph Neural Networks Spatial-Temporal Analysis Money Laundering Decentralized Finance Heterophily. Full Text Additional Declarations The authors declare no competing interests. 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-9369116","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":620349404,"identity":"b1980e62-9d5e-43ed-97a0-967a53d10f6a","order_by":0,"name":"Mahitha Geddavalasa","email":"data:image/png;base64,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","orcid":"","institution":"Vellore Institute of Technology, Amaravati","correspondingAuthor":true,"prefix":"","firstName":"Mahitha","middleName":"","lastName":"Geddavalasa","suffix":""}],"badges":[],"createdAt":"2026-04-09 13:16:55","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9369116/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9369116/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106613834,"identity":"b3207e68-e4fb-4fd0-9431-8007367c1bf8","added_by":"auto","created_at":"2026-04-10 12:49:30","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":201625,"visible":true,"origin":"","legend":"","description":"","filename":"FraudDetectionBlockchainMahitha.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9369116/v1_covered_5fee12e7-dd7d-4586-9270-56034459ca55.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eGraph Analytics for Blockchain Fraud Detection: A Comprehensive Review and the GraphGuard Framework\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Blockchain Security, Fraud Detection, Graph Neural Networks, Spatial-Temporal Analysis, Money Laundering, Decentralized Finance, Heterophily.","lastPublishedDoi":"10.21203/rs.3.rs-9369116/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9369116/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe decentralized nature of blockchain ecosystems, while fostering transparency and trustless transactions, has simultaneously facilitated the rise of complex fraudulent activities. From multi-hop phishing and Ponzi schemes to sophisticated money laundering operations, the scale of illicit financial flows in cryptocurrency networks has become a significant challenge for regulatory compliance and network security. Traditional machine learning (ML) methodologies, which rely on handcrafted statistical features and local heuristics, often fail to capture the complex relational and structural dependencies inherent in large-scale transaction graphs. This paper provides a comprehensive review of blockchain fraud detection through the lens of graph analytics, with a specific focus on the evolution toward Graph Neural Networks (GNNs). We identify a critical research gap: the inability of existing models to effectively handle \"camouflage\" behavior where fraudsters intentionally interact with benign entities to obscure their structural identity. 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