Structure of Co-offending Networks
Co-offending networks are generally extracted from police recorded crime data. For doing so, we need to have a clear view of crime data. In this chapter, we first introduce a unified formal model of crime data as a semantic framework for defining in an unambiguous way the meaning of co-offending networks at an abstract level. Then, we introduce a real-world crime dataset, referred to as BC crime dataset which is used in this book, and the BC co-offending network which is extracted from this dataset. The BC crime dataset represents 5 years of police arrest-data for the regions of the Province of British Columbia which are policed by the RCMP, comprising several million data records.
- 13.D.M.A. Hussain, D. Ortiz-Arroyo, Locating key actors in social networks using bayes posterior probability framework. Intell. Secur. Inform. 5376, 27–38 (2008)Google Scholar
- 14.R. Kumar, J. Novak, A. Tomkins, Structure and evolution of online social networks, in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’06) (2006), pp. 611–617Google Scholar
- 15.J. Leskovec, J. Kleinberg, C. Faloutsos, Graph evolution: densification and shrinking diameters. ACM Trans. Knowl. Discov. Data 1 (1), 2 (2007)Google Scholar
- 17.C.R. Palmer, P.B. Gibbons, C. Faloutsos, Anf: a fast and scalable tool for data mining in massive graphs, in Proceedings of the 8th ACM SIGKDD International Conference on Knowledge discovery and Data Mining (KDD’02) (2002), pp. 81–90Google Scholar
- 21.M.A. Tayebi, L. Bakker, U. Glässer, V. Dabbaghian, Locating central actors in co-offending networks, in Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining (ASONAM’11) (2011), pp. 171–179Google Scholar