Link Prediction Methods for Complex Illicit Networks: A Systematic Review

Link Prediction Methods for Complex Illicit Networks: A Systematic Review | 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 Systematic Review Link Prediction Methods for Complex Illicit Networks: A Systematic Review Hasini Balasuriya, Monica Gentili, Naoru Koizumi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9431584/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 Illicit trades, organized crime, trafficking, and terrorism remain major threats to global security, yet the networks that sustain these activities are difficult to study because available data are typically secondary, incomplete, and strategically con- cealed. In such settings, link prediction becomes important for inferring missing or unobserved relationships from network structure and associated attributes. This paper presents a systematic review focused on link prediction in illicit net- works. We synthesize the network representations, input features, models, and evaluation practices used across the existing literature, and critically examine how different link prediction methods have been adapted to the complex, covert, dynamic, and incomplete nature of illicit systems. The review shows that current studies span a wide range of illicit contexts and employ diverse methodological approaches, from similarity-based indices to graph neural networks and learning- based methods. However, the literature remains fragmented and is still dominated by static formulations, limited data settings, and evaluation strategies that do not fully reflect real-world investigative conditions. We identify major methodological and empirical gaps, including strong closed-world assumptions, and insufficient attention to robustness and uncertainty. The review concludes by outlining key directions for future research aimed at developing more realistic, interpretable, and operationally useful link prediction models for illicit-network analysis. link prediction illicit networks dynamic networks social network analysis 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. 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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-9431584","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":623925528,"identity":"68ae9ac1-3c20-47d2-bd12-bd25f5c27815","order_by":0,"name":"Hasini Balasuriya","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYBACAwYGNgaGAgYefvbmBgiXnRnIYGAmoMWAQU6y5yBUCzMjcVqMDW4kNkCECGkxFzv87MEPA7vEhpsPGx/zFNQl9jEzNj5gqLCGmYABLGenmRv2GCQnNs5ObDbmMTic2MbM2GzAcCYdpxaD2wlmEjwGzInN0olt0jwGB4yBfmmTYGw7jEdL+jfJPwb1iW2SB0Fa6kBa2n8w/sOnJccMqPKwMQ/QZCCDWQ5kCwNjA14t5cYyBsflJHgSmw3nGBwGaWmWSDiWbozHYdsevqmo5rE/fvjggzd/6njk25sPfvhQYy2LSwsOkECa8lEwCkbBKBgFaAAArpFTwfJR2EUAAAAASUVORK5CYII=","orcid":"","institution":"University of Louisville","correspondingAuthor":true,"prefix":"","firstName":"Hasini","middleName":"","lastName":"Balasuriya","suffix":""},{"id":623925529,"identity":"be84d599-a00b-4e41-b45d-d0dc5080f0c2","order_by":1,"name":"Monica Gentili","email":"","orcid":"","institution":"University of Louisville","correspondingAuthor":false,"prefix":"","firstName":"Monica","middleName":"","lastName":"Gentili","suffix":""},{"id":623925530,"identity":"a43ec465-b36c-463c-a33b-1925a11f14b8","order_by":2,"name":"Naoru Koizumi","email":"","orcid":"","institution":"George Mason University","correspondingAuthor":false,"prefix":"","firstName":"Naoru","middleName":"","lastName":"Koizumi","suffix":""}],"badges":[],"createdAt":"2026-04-16 00:31:34","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-9431584/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9431584/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107481881,"identity":"c10791d2-9876-4182-9cb6-a562834f91f8","added_by":"auto","created_at":"2026-04-22 02:20:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":526961,"visible":true,"origin":"","legend":"","description":"","filename":"SNAMMainManuscriptBlinded2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9431584/v1_covered_62b1b849-092b-46a3-aeda-07357ed583da.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eLink Prediction Methods for Complex Illicit Networks: A Systematic Review\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Louisville","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":"link prediction, illicit networks, dynamic networks, social network analysis","lastPublishedDoi":"10.21203/rs.3.rs-9431584/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9431584/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIllicit trades, organized crime, trafficking, and terrorism remain major threats to global security, yet the networks that sustain these activities are difficult to study because available data are typically secondary, incomplete, and strategically con- cealed. 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