Correlations Among Game of Thieves and Other Centrality Measures in Complex Networks

Ficara, Annamaria; Fiumara, Giacomo; Meo, Pasquale De; Liotta, Antonio

doi:10.1007/978-3-030-67197-6_3

Cited by 11 publications

(10 citation statements)

References 32 publications

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“…The degree k i allows to compute the clustering coefficient C i of a node i [ 31 ], which captures the degree to which the neighbors of the node i link to each other, given by where L i represents the number of links between the k i neighbors of node i . The average of C i over all nodes defined the average clustering coefficient 〈 C i 〉, measuring the probability that two neighbors of a randomly selected node link to each other.…”

Section: Methodsmentioning

confidence: 99%

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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%

“…The degree k i allows to compute the clustering coefficient C i of a node i [31], which captures the degree to which the neighbors of the node i link to each other, given by…”

Section: Plos Onementioning

confidence: 99%

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

“…Random networks try to reproduce the features of real networks by creating and populating random graphs. Among the most extensively used random network models [39] there are Erdös-Rényi (ER), Watts-Strogatz (WS), Barabási-Albert (BA) and Extended Barabási-Albert (EBA).…”

Section: Network Modelsmentioning

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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“…The degree k i allows to compute the clustering coefficient C i of a node i [30], which captures the degree to which the neighbors of the node i link to each other, given by…”

Section: Graph Propertiesmentioning

confidence: 99%

Criminal Networks Analysis in Missing Data scenarios through Graph Distances

Ficara,

Cavallaro,

Curreri

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Data collected in criminal investigations may suffer from: (i) incompleteness, due to the covert nature of criminal organisations; (ii) incorrectness, caused by either unintentional data collection errors and 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 analyse 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 following two specific methods: (i) random edges removal, simulating the scenario in which the Law Enforcement Agencies (LEAs) fail to intercept some calls, or to spot sporadic meetings among suspects; (ii) nodes removal, that catches the hypothesis in which some suspects cannot be intercepted or investigated. Finally we compute spectral (i.e., Adjacency, Laplacian and Normalised Laplacian Spectral Distances) and matrix (i.e., Root Euclidean Distance) distances between the complete and pruned networks, which we compare using statistical analysis. Our investigation identified two main features: first, the overall understanding of the criminal networks remains high even with incomplete data on criminal interactions (i.e., 10% removed edges); second, removing even a small fraction of suspects not investigated (i.e., 2% removed nodes) may lead to significant misinterpretation of the overall network.

show abstract

Correlations Among Game of Thieves and Other Centrality Measures in Complex Networks

Cited by 11 publications

References 32 publications

Criminal networks analysis in missing data scenarios through graph distances

Criminal networks analysis in missing data scenarios through graph distances

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

Criminal Networks Analysis in Missing Data scenarios through Graph Distances

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