Social Network Analysis of Sicilian Mafia Interconnections

Ficara, Annamaria; Cavallaro, Lucia; Meo, Pasquale De; Fiumara, Giacomo; Catanese, Salvatore; Bagdasar, Ovidiu; Liotta, Antonio

doi:10.1007/978-3-030-36683-4_36

Cited by 24 publications

(27 citation statements)

References 18 publications

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“…Affiliates typically prefer to spread the information through a number of intermediaries, to minimize the risk of interception by non-family members. This is consistent with our earlier findings [35]. Ultimately, it would not make sense (and would be unwise) to send the same message multiple times through the same path, which is what Katz would help identifying.…”

Section: Plos Onesupporting

confidence: 90%

“…The present study goes well beyond the initial characterization of these datasets [35], investigating network robustness across different scenarios, pinpointing the most effective metric, and demonstrating an effective strategy to obtain a faster LCC size drop. We simulate two types of police operations: (i) arresting one criminal at a time (sequential node removal), and (ii) police raids (node block removal).…”

Section: Introductionmentioning

confidence: 82%

“…In this section we explain how we have created the datasets (which is fully available on GitHub repository https://github.com/lcucav/criminal-nets/tree/master/disruption) and on Zenodo (https://doi.org/10.5281/zenodo.3938818), generating two criminal networks, directly from Court data. We have previously reported a visual representation of both datasets as undirected and weighted graphs in [35]. In our datasets, each node represents an individual, whereas links show mutual interactions among the individuals.…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…In [35], we introduced a preliminary characterization, whereby we defined the weight distributions and conducted the shortest path length analysis. The weights distribution analysis unveiled a long tail of nodes, with just a few dominating ones.…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…Consequently, we were able to set up a much larger dataset than what we could have obtained by focusing on other court orders. We created two very different networks, capturing phone calls and physical meetings, respectively, which we have characterized in our recent conference paper [35].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Plos Onesupporting

confidence: 90%

Section: Introductionmentioning

confidence: 82%

Section: Dataset Descriptionmentioning

confidence: 99%

Section: Dataset Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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