Complicity Functions for Detecting Organized Crime Rings

  • E. Vicente
  • A. Mateos
  • A. Jiménez-Martín
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9880)


Graph theory is an evident paradigm for analyzing social networks, which are the main tool for collective behavior research, addressing the interrelations between members of a more or less well-defined community. Particularly, social network analysis has important implications in the fight against organized crime, business associations with fraudulent purposes or terrorism.

Classic centrality functions for graphs are able to identify the key players of a network or their intermediaries. However, these functions provide little information in large and heterogeneous graphs. Often the most central elements of the network (usually too many) are not related to a collective of actors of interest, such as be a group of drug traffickers or fraudsters. Instead, its high centrality is due to the good relations of these central elements with other honorable actors.

In this paper we introduce complicity functions, which are capable of identifying the intermediaries in a group of actors, avoiding core elements that have nothing to do with this group. These functions can classify a group of criminals according to the strength of their relationships with other actors to facilitate the detection of organized crime rings.

The proposed approach is illustrated by a real example provided by the Spanish Tax Agency, including a network of 835 companies, of which eight were fraudulent.


Multidimensional Scaling Social Network Analysis Geodesic Distance Business Association Complicity Function 
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.



The paper was supported by the project MTM2014-56949-C3-2-R.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Decision Analysis and Statistics Group, Departamento de Inteligencia ArtificialUniversidad Politécnica de MadridMadridSpain

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