Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bavelas, A., Barret, D.: An experimental approach to organizational communication. Personnel 27, 366–371 (1951)
Cohn, B.S., Marriott, M.: Networks and centers in the integration in Indian civilization. J. Soc. Res. I(1), 1–9 (1958)
Czepiel, J.A.: Word of mouth processes in the diffusion of a major technological innovation. J. Mark. Res. I, 1–9 (1974)
Eades, P.: A heuristic for graph drawing. Congressus Numeranti 42(11), 149–160 (1984)
Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD 1996 Proceedings (1996)
Faust, K., Wasserman, S.: Blockmodels: interpretation and evaluation. Soc. Netw. 14(1–9), 5–61 (1992)
Freeman, L.C.: Centrality in social networks conceptual clarification. Soc. Netw. 1, 215–239 (1978)
Fruchterman, T.M.J., Reingold, E.M.: Graph drawing by force-directed placement. Softw. Pract. Experience 21(11), 1129–1164 (1991)
Hu, Y.: Efficient and high quality force-directed graph. Math. J. 10(1), 37–71 (2005)
Kamada, T., Kawai, S.: An algorithm for drawing general undirected graphs. Inf. Process. Lett. 31(1), 7–15 (1989)
Karrer, B., Newman, M.E.J.: Stochastic block models and community structure in networks. Phys. Rev. E 83(1), 016107 (2011)
Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)
Kleinberg, J.: Authoritative sources in a hyperlinked environment. J. ACM 46(5), 604–632 (1999)
Langville, A.N., Meyer, C.D.: A survey of eigenvector methods for web information retrieval. SIAM Rev. 47(1), 135–161 (2005)
Lawrence, P., Sergey, B., Rajeev, M., Terry, W.: The pagerank citation ranking: bringing order to the web. Technical report, Stanford University (1998)
Leavit, H.J.: Some effects of certain communication pattern on group performance. Massachusetts Institute of Technology (1949)
Peixoto, T.P.: Hierarchical block structures and high-resolution model selection in large networks. Phys. Rev. X 4(1), 011047 (2014)
Pitts, F.R.: A graph theoretic approach to historical geography. Prof. Geogr. 17, 15–20 (1965)
Quigley, A., Eades, P.: FADE: graph drawing, clustering, and visual abstraction. In: Marks, J. (ed.) GD 2000. LNCS, vol. 1984, pp. 197–210. Springer, Heidelberg (2001)
Schaeffer, S.E.: Graph clustering. Comput. Sci. Rev. I, 27–64 (2007)
Smith, S.L.: Communication Pattern and the Adaptability of Task-Oriented Groups: An Experimental Study. Group Networks Laboratory, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge (1950)
Torgerson, W.S.: Multidimensional scaling: I. Theory and method. Psychometrika 17(4), 401–419 (1952)
Acknowledgments
The paper was supported by the project MTM2014-56949-C3-2-R.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Vicente, E., Mateos, A., Jiménez-Martín, A. (2016). Complicity Functions for Detecting Organized Crime Rings. In: Torra, V., Narukawa, Y., Navarro-Arribas, G., Yañez, C. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2016. Lecture Notes in Computer Science(), vol 9880. Springer, Cham. https://doi.org/10.1007/978-3-319-45656-0_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-45656-0_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-45655-3
Online ISBN: 978-3-319-45656-0
eBook Packages: Computer ScienceComputer Science (R0)