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

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Listed:
  • Lucia Cavallaro
  • Annamaria Ficara
  • Pasquale De Meo
  • Giacomo Fiumara
  • Salvatore Catanese
  • Ovidiu Bagdasar
  • Wei Song
  • Antonio Liotta

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.

Suggested Citation

  • Lucia Cavallaro & Annamaria Ficara & Pasquale De Meo & Giacomo Fiumara & Salvatore Catanese & Ovidiu Bagdasar & Wei Song & Antonio Liotta, 2020. "Disrupting resilient criminal networks through data analysis: The case of Sicilian Mafia," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-22, August.
  • Handle: RePEc:plo:pone00:0236476
    DOI: 10.1371/journal.pone.0236476
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    References listed on IDEAS

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    Cited by:

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    2. Annamaria Ficara & Francesco Curreri & Giacomo Fiumara & Pasquale De Meo & Antonio Liotta, 2022. "Covert Network Construction, Disruption, and Resilience: A Survey," Mathematics, MDPI, vol. 10(16), pages 1-43, August.
    3. Juanjuan Luo & Teng Fei & Meng Tian & Yifei Liu & Meng Bian, 2023. "Sensitivity metrics of complex network based on co-occurrence truth table: exemplified by a high-speed rail network," Journal of Geographical Systems, Springer, vol. 25(4), pages 519-538, October.
    4. Manzi, Deborah & Calderoni, Francesco, 2024. "The resilience of drug trafficking organizations: Simulating the impact of police arresting key roles," Journal of Criminal Justice, Elsevier, vol. 91(C).
    5. Annamaria Ficara & Giacomo Fiumara & Salvatore Catanese & Pasquale De Meo & Xiaoyang Liu, 2022. "The Whole Is Greater than the Sum of the Parts: A Multilayer Approach on Criminal Networks," Future Internet, MDPI, vol. 14(5), pages 1-21, April.
    6. Niles Breuer & Federico Varese, 2023. "The Structure of Trade-type and Governance-type Organized Crime Groups: A Network Study," The British Journal of Criminology, Centre for Crime and Justice Studies, vol. 63(4), pages 867-888.
    7. Liu, Qian & Wang, Jian & Zhao, Zhidan & Zhao, Na, 2022. "Relatively important nodes mining algorithm based on community detection and biased random walk with restart," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).

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