skip to main content
10.1145/2808797.2808827acmconferencesArticle/Chapter ViewAbstractPublication PageskddConference Proceedingsconference-collections
research-article

A System for Analyzing Criminal Social Networks

Authors Info & Claims
Published:25 August 2015Publication History

ABSTRACT

The influential members of a criminal organization are usually targeted by criminal investigators for removal or surveillance. Identifying and capturing these influential members will most likely to disrupt the organization. We propose in this paper a forensic analysis system called CLDRI that can identify the most influential members of a criminal organization. First, a network representing a criminal organization is built from Mobile Communication Data that belongs to the organization. In such a network, a vertex represents an individual criminal and an edge represents the communication attempts between two criminals. CLDRI employs formulas that quantify the degree of importance of each vertex in the network relative to all other vertices. We present these formulas through series of improvement refinements. All the formulas incorporate novel-weighting schemes for the edges of networks. We evaluated the quality of CLDRI by comparing it experimentally with two systems. Results showed improvement.

References

  1. Breiger, R. L. 2004. The analysis of social networks. In Handbook of Data Analysis, M. A. Hardy and A. Bryman, Eds. Sage Publications, London, U.K. 505--526.Google ScholarGoogle Scholar
  2. Breiger, R. L., Boorman, S. A., and Arabie, P. 1975. An algorithm for clustering relational data, with applications to social network analysis and comparison with multidimensional scaling. J. Math. Psych. 12, 328--383.Google ScholarGoogle ScholarCross RefCross Ref
  3. Baker, W. E. and Faulkner R. R. 1993. The social organization of conspiracy: Illegal networks in the heavy electrical equipment industry. Amer. Soc. Rev. 58, 837--860.Google ScholarGoogle ScholarCross RefCross Ref
  4. Burt, S. 1980. Models of network structure. Ann. Rev. Soc. 6, 79--141.Google ScholarGoogle Scholar
  5. Baldi, P. & Hatfield, W. (2002), DNA Microarrays and Gene Expression, Cambridge University Press, Cambridge, UK. {80}.Google ScholarGoogle ScholarCross RefCross Ref
  6. Enron Corporation: https://www.uwosh.edu/llce/conted/lir/courselistings/Enron%20Scandal.pdf.Google ScholarGoogle Scholar
  7. E. Ferrara, P. De Meo, S. Catanese, and G. Fiumara, "Detecting criminal organizations in mobile phone networks," Expert Systems with Applications, vol. 41, no. 13, pp. 5733--5750, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  8. Enron Email Dataset. Available at: http://www-2.cs.cmu.edu/~enron/.Google ScholarGoogle Scholar
  9. Girvan, M., & Newman, M. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences, 99(12), 7821.Google ScholarGoogle ScholarCross RefCross Ref
  10. Glsser, Estimating Possible Criminal Organizations from Co-offending Data. Public Safety Canada, 2012.Google ScholarGoogle Scholar
  11. H. Sarvari, E. Abozinadah, A. Mbaziira, and D. McCoy, "Constructing and analyzing criminal networks," CA, USA, May 2014, pp. 84--91. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. H. Wang, C. K. Chang, H.-I. Yang, and Y. Chen, "Estimating the relative importance of nodes in social networks," Journal of Information Processing, vol. 21, no. 3, pp. 414--422, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  13. J. J. Xu and H. Chen, "CrimeNet explorer: A framework for criminal network knowledge discovery," ACM Trans. Inf. Syst., vol. 23, no. 2, pp. 201--226, Apr. 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Keila, P.S. and D.B. Skillicorn (2005), 'Structure in the Enron email dataset', Computational & Mathematical Organization Theory, 11(3), 183--99. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. L. C. Freeman, "Centrality in social networks conceptual clarification," Social networks, vol. 1, no. 3, pp. 215--239, 1979.Google ScholarGoogle ScholarCross RefCross Ref
  16. L. Cavique, A. B. Mendes, and J. M. Santos, "An algorithm to discover the k-clique cover in networks," Artificial Intelligence. Springer, 2009, pp. 363--373. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. L. Langohr, "Methods for finding interesting vertices in weighted graphs," Ph.D. dissertation, 2014.Google ScholarGoogle Scholar
  18. McAndrew, D. 1999. The structural analysis of criminal networks. In The Social Psychology of Crime: Groups, Teams, and Networks. D. Canter and L. Alison, Eds. Dartmouth Publishing, Aldershot, UK, 53--94.Google ScholarGoogle Scholar
  19. M. Akbas, R. Avula, M. Bassiouni, and D. Turgut, "Social network generation and friend ranking based on mobile phone data," Jun. 2013, pp. 1444--1448.Google ScholarGoogle Scholar
  20. Newman, M. (2004). Fast algorithm for detecting community structure in networks. Physical Review E, 69(6), 066133.Google ScholarGoogle Scholar
  21. Pattillo, N. Youssef, and S. Butenko, "Clique relaxation models in social network analysis," in Handbook of Optimization in Complex Networks. Springer, 2012, pp. 143--162.Google ScholarGoogle Scholar
  22. Taha, K. "Determining the Semantic Similarities among Gene Ontology Terms". IEEE Journal of Biomedical and Health Informatics (IEEE JBHI), 2013, Vol. 17, Issue 3, pp. 512 - 525.Google ScholarGoogle ScholarCross RefCross Ref
  23. Taha, K., Homouz, D., Al Muhairi, H., and Al Mahmoud, Z. "GRank: A Middleware Search Engine for Ranking Genes by {502} Relevance to Given Genes". BMC Bioinformatics 2013, 14:251, doi:10.1186/1471-2105-14-251.Google ScholarGoogle Scholar
  24. Tversky A: Features of Similarity. Psycholog. Rev 1977, 84:327-352.Google ScholarGoogle Scholar
  25. Taha, K. and Elmasri, R. "SPGProfile: Speak Group Profile." Information Systems (IS), 2010, Elsevier, Vol. 35, No. 7, pp. 774--790. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Taha, K. and Elmasri, R. "BusSEngine: A Business Search Engine." Knowledge and Information Systems: An International Journal (KAIS), 2010, LNCS, Springer, Vol. 23, No. 2, pp. 153--197. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Taha, K. and Elmasri, R. "CXLEngine: A Comprehensive XML Loosely Structured Search Engine." DataX'08 (Database technologies for handling XML information on the web), Nantes, France, March 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Taha, K. "Determining Semantically Related Significant Genes". IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2014, Vol. 11, issue 6, pp. 1119 - 1130. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Taha, K. "GRtoGR: A System for Mapping GO Relations to Gene Relations". IEEE Transactions on NanoBioscience, 2013, Vol. 12, Issue 4.Google ScholarGoogle ScholarCross RefCross Ref
  30. U. Glsser, Estimating Possible Criminal Organizations from Co-offending Data. Public Safety Canada, 2012.Google ScholarGoogle Scholar
  31. Wellman, B. 1988. Structural analysis: From method and metaphor to theory and substance. In Social structures: A network approach, B. Wellman and S. D. Berkowitz, Eds. Cambridge University Press, Cambridge, UK, 19--61.Google ScholarGoogle Scholar
  32. White, H. C., Boorman, S. A., and Breiger, R. L. 1976. Social structure from multiple networks: I. Blockmodels of roles and positions. Amer. J. Soc. 81, 730--780.Google ScholarGoogle ScholarCross RefCross Ref
  1. A System for Analyzing Criminal Social Networks

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015
        August 2015
        835 pages
        ISBN:9781450338547
        DOI:10.1145/2808797

        Copyright © 2015 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 25 August 2015

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article
        • Research
        • Refereed limited

        Acceptance Rates

        Overall Acceptance Rate116of549submissions,21%

        Upcoming Conference

        KDD '24

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader