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    Using the spanning tree of a criminal network for identifying its leaders

    Taha, K. and Yoo, Paul D. (2016) Using the spanning tree of a criminal network for identifying its leaders. IEEE Transactions on Information Forensics and Security 12 (2), pp. 445-453. ISSN 1556-6013.

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    Abstract

    We introduce a forensic analysis system called ECLfinder that identifies the influential members of a criminal organization as well as the immediate leaders of a given list of lower-level criminals. Criminal investigators usually seek to identify the influential members of criminal organizations, because eliminating them is most likely to hinder and disrupt the operations of these organizations and put them out of business. First, ECLfinder constructs a network representing a criminal organization from either mobile communication data associated with the organization or crime incident reports that include information about the organization. It then constructs a minimum spanning tree (MST) of the network. It identifies the influential members of a criminal organization by determining the impor- tant vertices in the network representing the organization, using the concept of existence dependence. Each vertex v is assigned a score, which is the number of other vertices, whose existence in MST is dependent on v. Vertices are ranked based on their scores. Criminals represented by the top ranked vertices are considered the influential members of the criminal organization represented by the network. We evaluated the quality of ECLfinder by comparing it experimentally with three other systems. Results showed marked improvement.

    Metadata

    Item Type: Article
    Additional Information: (c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Paul Yoo
    Date Deposited: 15 Oct 2018 14:39
    Last Modified: 30 Jul 2019 05:49
    URI: http://eprints.bbk.ac.uk/id/eprint/24454

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