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Co-offending Network Mining

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Abstract

We propose here a computational framework for co-offending network mining defined in terms of a process that combines formal data modeling with data mining of large crime and terrorism data sets as gathered and maintained by law enforcement and intelligence agencies. Our crime data analysis aims at exploring relevant properties of criminal networks in arrest-data and is based on 5 years of real-world crime data that was made available for research purposes. This data was retrieved from a large database system with several million data records keeping information for the regions of the Province of British Columbia. Beyond application of innovative data mining techniques for the analysis of the crime data set, we also provide a comprehensive data model applicable to any such data set and link the data model to the analysis techniques. We contend that central aspects considered in the work presented here carry over to a wide range of large data sets studied in intelligence and security informatics to better serve law enforcement and intelligence agencies.

Keywords

Cluster Coefficient Property Crime Link Prediction Crime Type Eigenvector Centrality 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Notes

Acknowledgements

We are thankful to RCMP “E” Division and BC Ministry for Public Safety and Solicitor General for making this research possible by providing Simon Fraser University with crime data from their Police Information Retrieval System. We also like to thank the anonymous reviewer(s) for their constructive criticism and helpful comments on an earlier version of our manuscript for this chapter.

References

  1. 1.
    Abbasi, A., Chen, H.: Applying authorship analysis to extremist-group Web forum messages. IEEE Intell. Syst. 20(5), 67–75 (2005)CrossRefGoogle Scholar
  2. 2.
    Adderley, R., Musgrove, P.: Modus operandi modelling of group offending: A data-mining case study. Int. J. Police Sci. Manage. 5(4), 265–276 (2003)CrossRefGoogle Scholar
  3. 3.
    Barabasi, A.L., Albert, R.: Emergence of scaling in random networks. Science 286 (1999)Google Scholar
  4. 4.
    Barabasi, A.L., Jeonga, H., Neda, Z., Ravasz, E., Schubert, A., Vicsek, T.: Evolution of the social network of scientific collaborations. Physica 311, 590614 (2002)Google Scholar
  5. 5.
    Brandes, U., Erlebach, T.: Fundamentals. In: Network Analysis: Methodological Foundations. Springer, Berlin (2005)Google Scholar
  6. 6.
    Brantingham, P.L.: Crime pattern theory. In: Fisher, B., Lab, S. (eds.) Encyclopedia of Victimology and Crime Prevention. Sage Publishing, Beverly Hills (2010)Google Scholar
  7. 7.
    Brantingham, P.L., Gl¨asser, U., Jackson, P., Kinney, B., Vajihollahi, M.: Mastermind: Computational modeling and simulation of spatiotemporal aspects of crime in Urban environments. In: Liu, L., Eck, J. (eds.) Artificial Crime Analysis Systems: Using Computer Simulations and Geographic Information Systems. IGI Global, Hershey, PA (2008)Google Scholar
  8. 8.
    Brantingham, P.L., Gl¨asser, U., Jackson, P., Vajihollahi, M.: Modeling criminal activity in Urban landscapes. In: Memon, N., et al. (eds.) Mathematical Methods in Counterterrorism. Springer, Berlin (2009)Google Scholar
  9. 9.
    Bruinsma, G., Bernasco, W.: Criminal groups and transnational illegal markets. Crime Law Soc. Change 41(1) (2004)Google Scholar
  10. 10.
    Chen, J., Zaine, O.R., Goebel, R.: Detecting communities in social networks using max-min modularity. In: Proceedings of the SIAM International Conference on Data Mining, SDM 2009, Sparks, Nevada, USA (2009)Google Scholar
  11. 11.
    Chakrabarti, D., Faloutsos, C.: Graph mining: Laws, generators, and algorithms. ACM Comput. Surv. 38(1) (2006)Google Scholar
  12. 12.
    Clauset, A., Shalizi, C.R., Newman, M.E.J.: Power-law distributions in empirical data. http://arxiv.org/abs/0706.1062v1. (2007)
  13. 13.
    Dijkstra, E.: A note on two problems in connection with graphs. Numer.Math. 1 269271 (1959)Google Scholar
  14. 14.
    Erdos, P., Renyi, A.: On random graphs. Publ. Math. 6 (1959)Google Scholar
  15. 15.
    Freeman, L.C.: Visualizing social networks. J. Soc. Struct. 1(1) (2000)Google Scholar
  16. 16.
    Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. USA 99 7821–7826 (2002)CrossRefzbMATHMathSciNetGoogle Scholar
  17. 17.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann, San Francisco, CA (2006)Google Scholar
  18. 18.
    Hauck, R.V., Atabakhsh, H., Ongvasith, P., Gupta, H., Chen, H.: Using Coplink to analyze criminal-justice data. IEEE Comput. 35(3), 3037 (2002)CrossRefGoogle Scholar
  19. 19.
    Leskovec, J., Kleinberg, J.M., Faloutsos, C.: Graph evolution: Densification and shrinking diameters. ACM TKDD 1(1), 2 (2007)CrossRefGoogle Scholar
  20. 20.
    Liben-Nowell, D., Kleinberg, J.M.: The link prediction problem for social networks. In: Proceedings of the Twelfth Annual ACM International Conference on Information and Knowledge Management, CIKM 2003, Nov 2003Google Scholar
  21. 21.
    Liu, L., Eck, J. (eds.): Artificial Crime Analysis Systems: Using Computer Simulations and Geographic Information Systems. IGI Global, Hershey, PA (2008)Google Scholar
  22. 22.
    Malm, A., Bichler, G., Van de Walle, S.: Comparing the ties that bind criminal networks: Is blood thicker than water?. Secur. J. 23, 5274 (2010)CrossRefGoogle Scholar
  23. 23.
    McGloin, J.M., Sullivan, C.J., Piquero, A.R., Bacon, S.: Investigating the stability of cooffending and co-offenders among a sample of youthful offenders. Criminology 46(1) (2008)Google Scholar
  24. 24.
    Memon, N., Farley, J.D., Hicks, D.L., Rosenorn, T. (eds.): Mathematical Methods in Counterterrorism. Springer, New York (2009)zbMATHGoogle Scholar
  25. 25.
    Kaza, S., Xu, J., Marshall, B., Chen, H.: Topological analysis of criminal activity networks: Enhancing transportation security. IEEE Trans. Intell. Transform. Syst. 10(1) (2009)Google Scholar
  26. 26.
    Kempe, D., Kleinberg, J.M., Tardos, E.: Influential nodes in a diffusion model for social networks. In: Proceedings of Automata, Languages and Programming, 32nd International Colloquium, ICALP 2005, Lisbon, Portugal (2005)Google Scholar
  27. 27.
    Kumar, R., Novak, J., Tomkins, A.: Structure and evolution of online social networks. In: KDD 06: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, 2006Google Scholar
  28. 28.
    Palmer, C., Gibbons, P., Faloutsos, C.: ANF: A fast and scalable tool for data mining in massive graphs. In: SIGKDD (2002)Google Scholar
  29. 29.
    Reiss, A.J.: Co-offending and criminal careers. Crime Justice: A Review of Research. University of Chicago Press, Chicago (1988)Google Scholar
  30. 30.
    Reiss, A.J., Farrington, D.P.: Advancing knowledge about co-offending: Results from a prospective longitudinal survey of London males. J. Crim. Law Criminol. 82(2) (1991)Google Scholar
  31. 31.
    Rossmo, D.K.: Geographic Profiling. CRC, New York (2000)Google Scholar
  32. 32.
    Short, M.B., Brantingham, P.J., Bertozzi, A.L., Tita, G.E.: Dissipation and displacement of hotspots in reaction-diffusion models of crime. PNAS 107, 3961–3965 (2010)CrossRefGoogle Scholar
  33. 33.
    Smith,M.N., King, P.J.H.: Incrementally visualising criminal networks. In: Sixth International Conference on Information Visualisation (IV’02), iv, pp. 76. (2002)Google Scholar
  34. 34.
    Valente, T.W.: Social Networks and Health: Models, Methods and Applications. Oxford University Press, New York (2010)Google Scholar
  35. 35.
    Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge (1994)CrossRefGoogle Scholar
  36. 36.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of small-world networks. Nature 393 (1998)Google Scholar
  37. 37.
    Xu, J.J., Chen, H.: Untangling Criminal Networks: A Case Study. In: ISI 2003, pp. 232–248. (2003)Google Scholar
  38. 38.
    Xu, J.J., Chen, H.: CrimeNet explorer: A framework for criminal network knowledge discovery. ACM Trans. Inform. Syst. 23(2), 201–226 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag/Wien 2011

Authors and Affiliations

  1. 1.School of CriminologySimon Fraser UniversityVancouverCanada
  2. 2.School of Computing ScienceSimon Fraser UniversityVancouverCanada

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