Show simple item record

dc.contributor.authorIsah, Haruna*
dc.contributor.authorNeagu, Daniel*
dc.contributor.authorTrundle, Paul R.*
dc.date.accessioned2016-12-09T16:06:09Z
dc.date.available2016-12-09T16:06:09Z
dc.date.issued2015-08
dc.identifier.citationIsah H, Neagu D and Trundle P (2015) Bipartite Network Model for Inferring Hidden Ties in Crime Data. In: Proceedings of the 2015 IEEE/ACM International Conference in Social Networks Analysis an Mining. 25-28 Aug 2015, Paris, France. ACM: 994-1001.en_US
dc.identifier.urihttp://hdl.handle.net/10454/10919
dc.descriptionNoen_US
dc.description.abstractCertain crimes are difficult to be committed by individuals but carefully organised by group of associates and affiliates loosely connected to each other with a single or small group of individuals coordinating the overall actions. A common starting point in understanding the structural organisation of criminal groups is to identify the criminals and their associates. Situations arise in many criminal datasets where there is no direct connection among the criminals. In this paper, we investigate ties and community structure in crime data in order to understand the operations of both traditional and cyber criminals, as well as to predict the existence of organised criminal networks. Our contributions are twofold: we propose a bipartite network model for inferring hidden ties between actors who initiated an illegal interaction and objects affected by the interaction, we then validate the method in two case studies on pharmaceutical crime and underground forum data using standard network algorithms for structural and community analysis. The vertex level metrics and community analysis results obtained indicate the significance of our work in understanding the operations and structure of organised criminal networks which were not immediately obvious in the data. Identifying these groups and mapping their relationship to one another is essential in making more effective disruption strategies in the future.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttp://dx.doi.org/10.1145/2808797.2808842en_US
dc.subjectCriminal groups; Datasets; Identification; Crime data; Cyber criminalsen_US
dc.titleBipartite Network Model for Inferring Hidden Ties in Crime Dataen_US
dc.status.refereedYesen_US
dc.typeConference paperen_US
dc.type.versionNo full-text in the repositoryen_US


This item appears in the following Collection(s)

Show simple item record