Abstract
Compared to other types of social networks, criminal networks present particularly hard challenges, due to their strong resilience to disruption, which poses severe hurdles to Law-Enforcement Agencies (LEAs). Herein, we borrow methods and tools from Social Network Analysis (SNA) to (i) unveil the structure and organization of Sicilian Mafia gangs, based on two real-world datasets, and (ii) gain insights as to how to efficiently reduce the Largest Connected Component (LCC) of two networks derived from them. Mafia networks have peculiar features in terms of the links distribution and strength, which makes them very different from other social networks, and extremely robust to exogenous perturbations. Analysts also face difficulties in collecting reliable datasets that accurately describe the gangs’ internal structure and their relationships with the external world, which is why earlier studies are largely qualitative, elusive and incomplete. An added value of our work is the generation of two real-world datasets, based on raw data extracted from juridical acts, relating to a Mafia organization that operated in Sicily during the first decade of 2000s. We created two different networks, capturing phone calls and physical meetings, respectively. Our analysis simulated different intervention procedures: (i) arresting one criminal at a time (sequential node removal); and (ii) police raids (node block removal). In both the sequential, and the node block removal intervention procedures, the Betweenness centrality was the most effective strategy in prioritizing the nodes to be removed. For instance, when targeting the top 5% nodes with the largest Betweenness centrality, our simulations suggest a reduction of up to 70% in the size of the LCC. We also identified that, due the peculiar type of interactions in criminal networks (namely, the distribution of the interactions’ frequency), no significant differences exist between weighted and unweighted network analysis. Our work has significant practical applications for perturbing the operations of criminal and terrorist networks.
Highlights
The Sicilian Mafia, known as Cosa Nostra, is a specific type of criminal organization
Our analysis identifies Betweenness centrality as the most effective metric, showing how, by neutralizing only 5% of the affiliates, the Largest Connected Component (LCC) size dropped by 70%
The LCC size drop velocity depends on Disrupting resilient criminal networks through data analysis the capability of centrality metrics to target the appropriate node with the right criterium, in terms of node importance, for the specific network topology
Summary
The Sicilian Mafia, known as Cosa Nostra, is a specific type of criminal organization It began in Sicily and grew into a major international criminal organization [1,2,3], taking control and influencing economic, social and political sectors of entire countries. Compared to other criminal organizations, Mafia has a unique structure: it appears as a collection of loosely coupled groups, which last for several generations [3, 4]. Each of these groups can be referred to as cosca (i.e., a Sicilian word which refers to any plant whose spiny closely folded leaves symbolize the tightness of relationships between members of the Mafia), gang, clan or family. Mafia tends to create deep roots into the very fabric of society, to the point that it becomes “impossible to destroy without a radical change in social institutions” (in the words of Italian politician Leopoldo Franchetti, 1876 [1])
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