Disrupting resilient criminal networks through data analysis: The case of Sicilian Mafia

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 realworld 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.

Social network analysis: the use of graph distances to compare artificial and criminal networks

“…The dataset we use in this paper can be downloaded from Zenodo [37] and has been presented and discussed in other papers [11,14,18] . We created it starting from the pre-trial detention order issued by the Preliminary Investigation Judge of the Court of Messina on March 14, 2007 We used the meeting and wiretaps described in the pre-trial detention order to create two networks.…”

“…As mentioned in [14] , we have chosen the pre-trial detention order associated with the Montagna Operation because of the large number of wiretaps and stakeout instances. To the best of our knowledge, the Montagna datasets contain are the largest among all court orders we had access to.…”

“…Indeed, the description of the structure of a criminal organization, the construction of crime prevention systems [11] , or the identification of the leaders within those kind of groups [12] are all features that can be described by the use of SNA. It is exploited by Law Enforcement Agencies (LEAs) as well to assess the impact of arrests in criminal organizations and the evolution of its structure as a reaction [13] , so to evaluate interventions aimed at dismantling and disrupting criminal networks [14] . Due to the difficulties in obtaining such kind of information, it is a unique opportunity to examine real datasets with the aim of using them to build mathematical objects (i.e., graphs or networks) and apply classical SNA tools on them [11,[13][14][15][16][17][18] .…”

“…Such models were then compared with a real criminal network dataset named Meetings. Such dataset, employed in our earlier works as well [11,14,18] , is publicly available on Zenodo [37] . This network describes the physical meetings among suspects in a police investigation called Montagna Operation.…”

Criminal networks analysis in missing data scenarios through graph distances

“…SNA relies on real datasets used as sources which allow to build networks that are then examined [10,[12][13][14][15][16][17][18]. However, the collection of complete network data describing the structure and activities of a criminal organization is difficult to obtain.…”