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dc.contributor.authorCavallaro, Lucia
dc.contributor.authorFicara, Annamaria
dc.contributor.authorDe Meo, Pasquale
dc.contributor.authorFiumara, Giacomo
dc.contributor.authorCatanese, Salvatore
dc.contributor.authorBagdasar, Ovidiu
dc.contributor.authorSong, Wei
dc.contributor.authorLiotta, Antonio
dc.identifier.citationCavallaro, L., Ficara, A., De Meo, P., Fiumara, G., Catanese, S., Bagdasar, O., Song, W. and Liotta, A., (2020). 'Disrupting resilient criminal networks through data analysis: The case of Sicilian Mafia'. PloS one, 15(8), pp. 1-22.en_US
dc.description.abstractCompared 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.en_US
dc.publisherPublic Library of Science (PLoS)en_US
dc.rightsCC0 1.0 Universal*
dc.subjectSocial network analysisen_US
dc.subjectResilient Networksen_US
dc.subjectTheoretical computer scienceen_US
dc.subjectInformation Technologyen_US
dc.titleDisrupting resilient criminal networks through data analysis: The case of Sicilian Mafiaen_US
dc.contributor.departmentUniversity of Derbyen_US
dc.contributor.departmentniversity of Palermo, Palermo, Italyen_US
dc.contributor.departmentUniversity of Messina, Polo Universitario Annunziata, Messina, Italyen_US
dc.contributor.departmentShanghai Ocean University, Shanghai, Chinaen_US
dc.contributor.departmentEdinburgh Napier Universityen_US
dc.identifier.journalPLoS ONEen_US
dc.source.journaltitlePLOS ONE

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