Criminal organizations tend to be clustered to reduce risks of detection and information leaks. Yet, the literature exploring the relevance of subgroups for their internal structure is so far very limited. The paper applies methods of community analysis to explore the structure of a criminal network representing the individuals’ co-participation in meetings. It draws from a case study on a large law enforcement operation (“Operazione Infinito”) tackling the ‘Ndrangheta, a mafia organization from Calabria, a southern Italian region. The results show that the network is indeed clustered and that communities are associated, in a non-trivial way, with the internal organization of the ‘Ndrangheta into different “locali” (similar to mafia families). Furthermore, the results of community analysis can improve the prediction of the “locale” membership of the criminals (up to two thirds of any random sample of nodes) and the leadership roles (above 90% precision in classifying nodes as either bosses or non-bosses). The implications of these findings on the interpretation of the structure and functioning of the criminal network are discussed.

Communities in criminal networks: A case study

BRUNETTO, DOMENICO;PICCARDI, CARLO
2017-01-01

Abstract

Criminal organizations tend to be clustered to reduce risks of detection and information leaks. Yet, the literature exploring the relevance of subgroups for their internal structure is so far very limited. The paper applies methods of community analysis to explore the structure of a criminal network representing the individuals’ co-participation in meetings. It draws from a case study on a large law enforcement operation (“Operazione Infinito”) tackling the ‘Ndrangheta, a mafia organization from Calabria, a southern Italian region. The results show that the network is indeed clustered and that communities are associated, in a non-trivial way, with the internal organization of the ‘Ndrangheta into different “locali” (similar to mafia families). Furthermore, the results of community analysis can improve the prediction of the “locale” membership of the criminals (up to two thirds of any random sample of nodes) and the leadership roles (above 90% precision in classifying nodes as either bosses or non-bosses). The implications of these findings on the interpretation of the structure and functioning of the criminal network are discussed.
2017
Centrality measures; Community analysis; Criminal networks; Dark networks; Leadership identification; Membership identification; Anthropology; Sociology and Political Science; Social Sciences (all); Psychology (all)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1007273
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