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The Whole Is Greater than the Sum of the Parts: A Multilayer Approach on Criminal Networks

Author

Listed:
  • Annamaria Ficara

    (MIFT Department, University of Messina, 98166 Messina, Italy)

  • Giacomo Fiumara

    (MIFT Department, University of Messina, 98166 Messina, Italy)

  • Salvatore Catanese

    (MIFT Department, University of Messina, 98166 Messina, Italy)

  • Pasquale De Meo

    (DICAM Department, University of Messina, 98168 Messina, Italy)

  • Xiaoyang Liu

    (School of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, China)

Abstract

Traditional social network analysis can be generalized to model some networked systems by multilayer structures where the individual nodes develop relationships in multiple layers. A multilayer network is called multiplex if each layer shares at least one node with some other layer. In this paper, we built a unique criminal multiplex network from the pre-trial detention order by the Preliminary Investigation Judge of the Court of Messina (Sicily) issued at the end of the Montagna anti-mafia operation in 2007. Montagna focused on two families who infiltrated several economic activities through a cartel of entrepreneurs close to the Sicilian Mafia. Our network possesses three layers which share 20 nodes. The first captures meetings between suspected criminals, the second records phone calls and the third detects crimes committed by pairs of individuals. We used measures from multilayer network analysis to characterize the actors in the network based on their local edges and their relevance to each specific layer. Then, we used measures of layer similarity to study the relationships between different layers. By studying the actor connectivity and the layer correlation, we demonstrated that a complete picture of the structure and the activities of a criminal organization can be obtained only considering the three layers as a whole multilayer network and not as single-layer networks. Specifically, we showed the usefulness of the multilayer approach by bringing out the importance of actors that does not emerge by studying the three layers separately.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jftint:v:14:y:2022:i:5:p:123-:d:797657
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    References listed on IDEAS

    as
    1. Paoli, Letizia, 2008. "Mafia Brotherhoods: Organized Crime, Italian Style," OUP Catalogue, Oxford University Press, number 9780195375268.
    2. David Bright & Catherine Greenhill & Thomas Britz & Alison Ritter & Carlo Morselli, 2017. "Criminal network vulnerabilities and adaptations," Global Crime, Taylor & Francis Journals, vol. 18(4), pages 424-441, October.
    3. David A. Bright & Catherine Greenhill & Alison Ritter & Carlo Morselli, 2015. "Networks within networks: using multiple link types to examine network structure and identify key actors in a drug trafficking operation," Global Crime, Taylor & Francis Journals, vol. 16(3), pages 219-237, July.
    4. Villani, Salvatore & Mosca, Michele & Castiello, Mauro, 2019. "A virtuous combination of structural and skill analysis to defeat organized crime," Socio-Economic Planning Sciences, Elsevier, vol. 65(C), pages 51-65.
    5. 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.
    6. Manlio De Domenico & Albert Solé-Ribalta & Elisa Omodei & Sergio Gómez & Alex Arenas, 2015. "Ranking in interconnected multilayer networks reveals versatile nodes," Nature Communications, Nature, vol. 6(1), pages 1-6, November.
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    Cited by:

    1. 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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