A graph neural network-based role classification in criminal networks CSCI–RTCW

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dc.contributor.authorDogan, Vedaten
dc.contributor.authorPrestwich, Steven D.en
dc.contributor.authorO'Sullivan, Barryen
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderEuropean Regional Development Funden
dc.date.accessioned2025-05-08T15:02:12Z
dc.date.available2025-05-08T15:02:12Z
dc.date.issued2024-12-12en
dc.description.abstractUnderstanding the roles of individuals in terrorist networks is an important task in counter-terrorism. This paper presents the first application of graph neural networks to this task. We apply our approach to a real-world terrorist network representing three different ideologies and nine specific groups. We demonstrate the challenges associated with this task and present the framework using graph neural networks and their advantages in this context.en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationDogan, V., Prestwich, S. and O'Sullivan, B. (2024) 'A graph neural network-based role classification in criminal networks CSCI–RTCW', 2024 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, December 11-13, 2024.en
dc.identifier.urihttps://hdl.handle.net/10468/17404
dc.language.isoenen
dc.relation.ispartof11th Annual Conf. on Computational Science & Computational Intelligence (CSCI'24)en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/Research Centres Programme::Phase 2/12/RC/2289_P2/IE/INSIGHT_Phase 2 /en
dc.rights© 2024, the Authors. This work is made available under the CC BY license (https://creativecommons.org/licenses/by/4.0/)en
dc.subjectTerrorist networksen
dc.subjectCounter-terrorismen
dc.subjectRole identificationen
dc.subjectGraph neural networksen
dc.subjectNode classificationen
dc.titleA graph neural network-based role classification in criminal networks CSCI–RTCWen
dc.typeArticle (peer-reviewed)en
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