Using shortest path to discover criminal community |
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| Institution: | 1. School of Mathematical and Geospatial Sciences, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia;2. Advanced Informatics School, Level 5, Menara Razak, Universiti Teknologi Malaysia, Jalan Semarak, 54100 Kuala Lumpur, Malaysia;1. Department of Mathematics and Statistics, University of Minnesota Duluth, Duluth, USA;2. Department of Applied Mathematics, VŠB – Technical University of Ostrava, Ostrava, Czech Republic;3. IT4Innovations National Supercomputing Center, VŠB – Technical University of Ostrava Ostrava, Czech Republic;1. CIICESI, ESTG, Polytechnic Institute of Porto, Portugal;2. Faculdade de Psicologia, Universidade de Lisboa, Lisbon, Portugal;3. Algoritmi Centre/Department of Informatics, University of Minho, Braga, Portugal;1. Center for Pediatric Trauma Research, The Research Institute at Nationwide Children''s Hospital, USA;2. College of Public Health, Division of Biostatistics, The Ohio State University, USA;3. Injury Research and Policy, Johns Hopkins Center for International Injury Research Unit, Johns Hopkins University, USA;4. Colorado Injury Control Research Center, Colorado State University, USA;5. Center for Injury Research and Policy, The Research Institute at Nationwide Children''s Hospital, USA;6. The Ohio State University College of Medicine, USA;1. Department of Mathematical Sciences, Middle Tennessee State University, Murfreesboro, TN 37132, United States;2. Department of Mathematics and Statistics, Texas Tech University, Lubbock, TX 79409, United States;3. Vanderbilt-Ingram Cancer Center, 771 Preston Building, 2220 Pierce Avenue, Nashville, TN 37232, United States |
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| Abstract: | Extracting communities using existing community detection algorithms yields dense sub-networks that are difficult to analyse. Extracting a smaller sample that embodies the relationships of a list of suspects is an important part of the beginning of an investigation. In this paper, we present the efficacy of our shortest paths network search algorithm (SPNSA) that begins with an ‘algorithm feed’, a small subset of nodes of particular interest, and builds an investigative sub-network. The algorithm feed may consist of known criminals or suspects, or persons of influence. This sets our approach apart from existing community detection algorithms. We apply the SPNSA on the Enron Dataset of e-mail communications starting with those convicted of money laundering in relation to the collapse of Enron as the algorithm feed. The algorithm produces sparse and small sub-networks that could feasibly identify a list of persons and relationships to be further investigated. In contrast, we show that identifying sub-networks of interest using either existing community detection algorithms or a k-Neighbourhood approach produces sub-networks of much larger size and complexity. When the 18 top managers of Enron were used as the algorithm feed, the resulting sub-network identified 4 convicted criminals that were not managers and so not part of the algorithm feed. We directly validate the SPNSA by removing one of the convicted criminals from the algorithm feed and re-running the algorithm; in 5 out of 9 cases the left out criminal occurred in the resulting sub-network. |
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| Keywords: | Criminal network Shortest path Leave-one-out Trust Suspect Investigation |
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