  Compared to other types of social networks, criminal networks present hard
challenges, due to their strong resilience to disruption, which poses severe
hurdles to law-enforcement agencies. Herein, we borrow methods and tools from
Social Network Analysis to (i) unveil the structure of Sicilian Mafia gangs,
based on two real-world datasets, and (ii) gain insights as to how to
efficiently disrupt them. Mafia networks have peculiar features, due to the
links distribution and strength, which makes them very different from other
social networks, and extremely robust to exogenous perturbations. Analysts are
also faced with the difficulty 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 derived 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 network disruption analysis simulated different intervention
procedures: (i) arresting one criminal at a time (sequential node removal); and
(ii) police raids (node block removal). We measured the effectiveness of each
approach through a number of network centrality metrics. We found Betweeness
Centrality to be the most effective metric, showing how, by neutralizing only
the 5% of the affiliates, network connectivity dropped by 70%. 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 tackling criminal and terrorist
networks.
