Criminal Network Analysis with Interactive Strategies: A Proof of Concept Study using Mobile Call Logs

The communication data are becoming increasingly important for criminal network analysis nowadays, this data provides a digital trace which can be regarded as a hidden clue to support the crack of criminal cases. Additionally, performing a timely and effective analysis on it can predict criminal intents and take efficient actions to restrain and prevent crimes. The primary work of our research is to suggest an analytical process with interactive strategies as a solution to the problem of characterizing criminal groups constructed from the communication data. It is expected to assist law enforcement agencies in the task of discovering the potential suspects and exploring the underlying structures of criminal network hidden behind the communication data. This process allows for network analysis with commonly used metrics to identify the core members. It permits exploration and visualization of the network in the goal of improving the comprehension of interesting microstructures. Most importantly, it also allows to extract community structures in a appropriate level with the label supervision strategy. Our work concludes illustrating the application of our interactive strategies to a real world criminal investigation with mobile call logs. Keywords-criminal network analysis; interactive strategy; network measure; community detection;

[1]  Juan Luis Cabrera,et al.  Weighting dissimilarities to detect communities in networks , 2015, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[2]  Valdis E. Krebs,et al.  Mapping Networks of Terrorist Cells , 2001 .

[3]  W. Baker,et al.  THE SOCIAL ORGANIZATION OF CONSPIRACY: ILLEGAL NETWORKS IN THE HEAVY ELECTRICAL EQUIPMENT INDUSTRY* , 1993 .

[4]  Malcolm K. Sparrow,et al.  The application of network analysis to criminal intelligence: An assessment of the prospects , 1991 .

[5]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[6]  Ladan Tahvildari,et al.  Cultural scene detection using reverse Louvain optimization , 2014, Sci. Comput. Program..

[7]  Mark Newman,et al.  Detecting community structure in networks , 2004 .

[8]  M E J Newman,et al.  Fast algorithm for detecting community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  Damon McCoy,et al.  Constructing and Analyzing Criminal Networks , 2014, 2014 IEEE Security and Privacy Workshops.

[10]  P. Klerks The Network Paradigm Applied to Criminal Organisations: Theoretical nitpicking or a relevant doctrine for investigators? Recent developments in the Netherlands , 2001 .

[11]  A. Silke The Devil You Know: Continuing Problems with Research on Terrorism , 2001 .

[12]  Pasquale De Meo,et al.  Detecting criminal organizations in mobile phone networks , 2014, Expert Syst. Appl..

[13]  T. S. Evans,et al.  Clique graphs and overlapping communities , 2010, ArXiv.

[14]  David W. Brannan,et al.  Talking to "Terrorists": Towards an Independent Analytical Framework for the Study of Violent Substate Activism , 2001 .

[15]  Mason A. Porter,et al.  Communities in Networks , 2009, ArXiv.