Multilayer Network Analysis: The Identification of Key Actors in a Sicilian Mafia Operation

Ficara, Annamaria; Fiumara, Giacomo; Meo, Pasquale De; Catanese, Salvatore

doi:10.1007/978-3-030-78459-1_9

Cited by 11 publications

(16 citation statements)

References 28 publications

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“…As we have already discussed in [5], a Mafia family or clan has a typical hierarchical structure. On top of the pyramid hierarchical chart is the Boss who keeps a low profile often hiding his real identity.…”

Section: Criminal Network Datasetmentioning

confidence: 91%

“…Thus, we built a labeled graph in which each node has an attribute as described in Table I. This attribute corresponds to the role of the node in the pyramidal hierarchical structure of Mafia clans [5]. Moreover, Table I also shows the number of nodes having that specific role in M M and M P C networks, respectively.…”

Section: Criminal Network Datasetmentioning

confidence: 99%

“…Herein, we borrow methods and tools from Social Network Analysis (SNA) to investigate the effectiveness of several law enforcement interventions against two Mafia networks, based on a real-world dataset built from a major anti-mafia operation called "Montagna" which was concluded in 2007. This dataset was used in different studies on Mafia networks through SNA to analyze the structure of such networks [3], [4], identify subgroups and highlight actors in strategic positions [5]- [7], and develop disruption and prevention methods [8]- [10].…”

Section: Introductionmentioning

confidence: 99%

“…The human capital strategy was implemented by removing actors possessing a particular resource and the highest degree centrality value. Our approach is different because our aim is to dismantle Mafia families, which have a very peculiar hierarchical structure [5], with different characteristics from simple criminal groups who produce and distribute synthetic drugs. In fact, our human capital strategy does not consist in targeting actors who possess a particular resource but those who have a particular role in the hierarchy of the Mafia group.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Human and Social Capital Strategies for Mafia Network Disruption

Ficara

Curreri

Fiumara

et al. 2023

IEEE Trans.Inform.Forensic Secur.

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Social Network Analysis (SNA) is an interdisciplinary science that focuses on discovering the patterns of individuals interactions. In particular, practitioners have used SNA to describe and analyze criminal networks to highlight subgroups, key actors, strengths and weaknesses in order to generate disruption interventions and crime prevention systems. In this paper, the effectiveness of a total of seven disruption strategies for two real Mafia networks is investigated adopting SNA tools. Three interventions targeting actors with a high level of social capital and three interventions targeting those with a high human capital are put to the test and compared between each other and with random node removal. Similar tests on artificial model networks have also been carried out. Simulations show that actor removal based on social capital proves to be the most effective strategy, by leading to the total disruption of the criminal network in the least number of steps. The removal of a specific figure of a Mafia family such as the Caporegime seems also promising in the network disruption.

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Section: Criminal Network Datasetmentioning

confidence: 91%

Section: Criminal Network Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Human and Social Capital Strategies for Mafia Network Disruption

Ficara

Curreri

Fiumara

et al. 2023

IEEE Trans.Inform.Forensic Secur.

Self Cite

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“…On the application side, a growing number of researchers are resorting to multilayer network models to study illicit supply chains [11,[25][26][27][28] since these models allow actors to be placed in different layers representing different characteristics of covert networks. Going beyond mapping, only the co-offending activity, multilayer models help to reveal more about the social processes that connect people, which can be used to infer missing information [29,30].…”

Section: Literature Reviewmentioning

confidence: 99%

Reconstructing Sparse Multiplex Networks with Application to Covert Networks

Bichler

et al. 2023

Entropy

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Network structure provides critical information for understanding the dynamic behavior of complex systems. However, the complete structure of real-world networks is often unavailable, thus it is crucially important to develop approaches to infer a more complete structure of networks. In this paper, we integrate the configuration model for generating random networks into an Expectation–Maximization–Aggregation (EMA) framework to reconstruct the complete structure of multiplex networks. We validate the proposed EMA framework against the Expectation–Maximization (EM) framework and random model on several real-world multiplex networks, including both covert and overt ones. It is found that the EMA framework generally achieves the best predictive accuracy compared to the EM framework and the random model. As the number of layers increases, the performance improvement of EMA over EM decreases. The inferred multiplex networks can be leveraged to inform the decision-making on monitoring covert networks as well as allocating limited resources for collecting additional information to improve reconstruction accuracy. For law enforcement agencies, the inferred complete network structure can be used to develop more effective strategies for covert network interdiction.

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