Robust link prediction in criminal networks: A case study of the Sicilian Mafia

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

doi:10.1016/j.eswa.2020.113666

Cited by 49 publications

(45 citation statements)

References 48 publications

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“…In both networks, nodes are suspected criminals and edges represent meetings (MN), or recorded phone calls (PC). These original datasets have been already studied in some of our previous works [ 10 , 16 – 18 , 29 , 45 ] and they are available on Zenodo [ 35 ].…”

Section: Methodsmentioning

confidence: 99%

“…SNA is indeed a powerful tool to analyze criminal networks and to gain a deeper understanding of criminal behavior [ 8 ]. SNA algorithms are able to produce relevant measurements and parameters relevant to identify the roles and importance of individuals within criminal organizations [ 9 ] and to construct crime prevention systems [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

“…SNA relies on real datasets used as sources which allow to build networks that are then examined [10,[12][13][14][15][16][17][18]. However, the collection of complete network data describing the structure and activities of a criminal organization is difficult to obtain.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Criminal networks analysis in missing data scenarios through graph distances

Ficara

Cavallaro²,

Curreri³

et al. 2021

PLoS ONE

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Data collected in criminal investigations may suffer from issues like: (i) incompleteness, due to the covert nature of criminal organizations; (ii) incorrectness, caused by either unintentional data collection errors or intentional deception by criminals; (iii) inconsistency, when the same information is collected into law enforcement databases multiple times, or in different formats. In this paper we analyze nine real criminal networks of different nature (i.e., Mafia networks, criminal street gangs and terrorist organizations) in order to quantify the impact of incomplete data, and to determine which network type is most affected by it. The networks are firstly pruned using two specific methods: (i) random edge removal, simulating the scenario in which the Law Enforcement Agencies fail to intercept some calls, or to spot sporadic meetings among suspects; (ii) node removal, modeling the situation in which some suspects cannot be intercepted or investigated. Finally we compute spectral distances (i.e., Adjacency, Laplacian and normalized Laplacian Spectral Distances) and matrix distances (i.e., Root Euclidean Distance) between the complete and pruned networks, which we compare using statistical analysis. Our investigation identifies two main features: first, the overall understanding of the criminal networks remains high even with incomplete data on criminal interactions (i.e., when 10% of edges are removed); second, removing even a small fraction of suspects not investigated (i.e., 2% of nodes are removed) may lead to significant misinterpretation of the overall network.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Criminal networks analysis in missing data scenarios through graph distances

Ficara

Cavallaro²,

Curreri³

et al. 2021

PLoS ONE

Self Cite

View full text Add to dashboard Cite

show abstract

“…Thus, SNA became a powerful analytical tool that allows to investigate on criminal organizations and to gain a deeper understanding of criminal behavior [10] . Indeed, the description of the structure of a criminal organization, the construction of crime prevention systems [11] , or the identification of the leaders within those kind of groups [12] are all features that can be described by the use of SNA. It is exploited by Law Enforcement Agencies (LEAs) as well to assess the impact of arrests in criminal organizations and the evolution of its structure as a reaction [13] , so to evaluate interventions aimed at dismantling and disrupting criminal networks [14] .…”

Section: Introductionmentioning

confidence: 99%

Social network analysis: the use of graph distances to compare artificial and criminal networks

Ficara¹,

Curreri²,

Cavallaro³

et al. 2021

JSEGC

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“…As a complex network data analysis tool, link prediction can be used to process and mine various types of network information, such as assisting scientists in conducting biological protein structure analysis experiments to discover the interactions between different amino acids [1], helping merchants in product recommendation systems to recommend products and services to potential customers [2], assisting data engineers in data processing to retain hidden links and clean up false links [3]. At the same time, link prediction can be used as a criminal investigation tool for network mining and analysis of criminal suspects [4].…”

Section: Introductionmentioning

confidence: 99%