Illicit Bitcoin Transaction Detection via Feature-Gated Temporal Graph Learning

Illicit Bitcoin Transaction Detection via Feature-Gated Temporal Graph Learning | 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 Illicit Bitcoin Transaction Detection via Feature-Gated Temporal Graph Learning Na Han, Ruixian Zhang, Xiaoyun Liu, Haining Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9165631/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract Illicit transaction detection is a key task in blockchain anti-money laundering, yet it remains challenging due to temporal graph evolution, severe class imbalance, and the difficulty of preserving informative transaction attributes during representation learning. Existing graph-based approaches have demonstrated the importance of temporal modeling on Bitcoin transaction networks, but their performance may still degrade when structural evidence becomes unstable or when minority illicit nodes are weakly supported by local graph context. To address these issues, we propose FG-EGCN, a feature-gated temporal graph model for illicit Bitcoin transaction detection. The proposed method combines temporal graph encoding with a residual feature branch and an adaptive gating mechanism, enabling the model to balance temporal-relational evidence and transaction-level attribute information in a node-aware manner. In addition, focal-loss-based optimization is adopted to improve sensitivity to rare illicit samples under highly imbalanced label distributions. We evaluate FG-EGCN on the Elliptic Bitcoin transaction dataset under a chronological train-test split. Experimental results show that FG-EGCN achieves strong performance among the compared graph neural models and delivers more robust illicit transaction detection under temporal distribution shift. Timestep-level analysis further demonstrates improved temporal stability, while ablation studies confirm the contribution of temporal evolution, feature preservation, adaptive gating, and imbalance-aware optimization. Moreover, representation visualization and gate analysis show that the proposed model produces a more structured and interpretable embedding space, in which illicit nodes are more compact and better separated from the dominant node population. These results indicate that combining temporal graph learning with feature preservation and adaptive fusion provides an effective and robust solution for illicit Bitcoin transaction detection. 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 08 May, 2026 Reviewers agreed at journal 07 May, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviews received at journal 29 Apr, 2026 Reviewers agreed at journal 28 Apr, 2026 Reviewers agreed at journal 28 Apr, 2026 Reviews received at journal 20 Apr, 2026 Reviewers agreed at journal 12 Apr, 2026 Reviewers agreed at journal 31 Mar, 2026 Reviewers invited by journal 26 Mar, 2026 Editor assigned by journal 26 Mar, 2026 Editor invited by journal 25 Mar, 2026 Submission checks completed at journal 23 Mar, 2026 First submitted to journal 23 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-9165631","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":612419525,"identity":"d8c017ec-98ed-4340-8ad8-87324d414fb6","order_by":0,"name":"Na Han","email":"","orcid":"","institution":"Shandong Vocational and Technical University of International Studies","correspondingAuthor":false,"prefix":"","firstName":"Na","middleName":"","lastName":"Han","suffix":""},{"id":612419526,"identity":"cef3ce26-86af-4a73-9f9e-68b88395c9bb","order_by":1,"name":"Ruixian Zhang","email":"","orcid":"","institution":"Shandong Vocational and Technical University of International Studies","correspondingAuthor":false,"prefix":"","firstName":"Ruixian","middleName":"","lastName":"Zhang","suffix":""},{"id":612419528,"identity":"b792f5e4-67dd-45db-82a3-dab54b585c99","order_by":2,"name":"Xiaoyun Liu","email":"","orcid":"","institution":"Shandong Vocational and Technical University of International Studies","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyun","middleName":"","lastName":"Liu","suffix":""},{"id":612419533,"identity":"52a3298a-d2a0-4717-a0d8-0310d7b48582","order_by":3,"name":"Haining Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYBACfmbG5gcJFRL1bMzMB6BiCfi1SLY3txl8OGOTwM/OBlJqQFiLwZnjDZIz29ISJPt5DIjTwnAjscGYt+1wnsFhns+fedv+MPCz5xgw/NyBWwfjjMSGxzznDhcbHObdJs3bZsAg2fPGgLH3DG4tzBJAW3jKDjNuAGphzgVqMbiRY8DM2IZbCxtQizQPG0gLz+PPIC32hLTw8BxskJzRlpY4s5mHQRpsiwQBLRLsjeBANuZnZjOT/nPOmEfizLOCg714tNgfZn8Miko5Nv7Djz/OKJOT429P3vjgJx4tmC4FEQdI0DAKRsEoGAWjAAsAAFlxUhxdCXS5AAAAAElFTkSuQmCC","orcid":"","institution":"Shandong Vocational and Technical University of International Studies","correspondingAuthor":true,"prefix":"","firstName":"Haining","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2026-03-19 06:24:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9165631/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9165631/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105567319,"identity":"bd104599-ff26-4020-bca9-96ca68da1321","added_by":"auto","created_at":"2026-03-27 12:58:55","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1966998,"visible":true,"origin":"","legend":"","description":"","filename":"scientificreports.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9165631/v1_covered_761e4b99-2b6a-455f-ac66-74270448664e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Illicit Bitcoin Transaction Detection via Feature-Gated Temporal Graph Learning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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-9165631/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9165631/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Illicit transaction detection is a key task in blockchain anti-money laundering, yet it remains challenging due to temporal graph evolution, severe class imbalance, and the difficulty of preserving informative transaction attributes during representation learning. Existing graph-based approaches have demonstrated the importance of temporal modeling on Bitcoin transaction networks, but their performance may still degrade when structural evidence becomes unstable or when minority illicit nodes are weakly supported by local graph context. To address these issues, we propose FG-EGCN, a feature-gated temporal graph model for illicit Bitcoin transaction detection. The proposed method combines temporal graph encoding with a residual feature branch and an adaptive gating mechanism, enabling the model to balance temporal-relational evidence and transaction-level attribute information in a node-aware manner. In addition, focal-loss-based optimization is adopted to improve sensitivity to rare illicit samples under highly imbalanced label distributions. We evaluate FG-EGCN on the Elliptic Bitcoin transaction dataset under a chronological train-test split. Experimental results show that FG-EGCN achieves strong performance among the compared graph neural models and delivers more robust illicit transaction detection under temporal distribution shift. Timestep-level analysis further demonstrates improved temporal stability, while ablation studies confirm the contribution of temporal evolution, feature preservation, adaptive gating, and imbalance-aware optimization. Moreover, representation visualization and gate analysis show that the proposed model produces a more structured and interpretable embedding space, in which illicit nodes are more compact and better separated from the dominant node population. 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