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Criminal networks analysis in missing data scenarios through graph distances

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 informati...

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Autores principales: Ficara, Annamaria, Cavallaro, Lucia, Curreri, Francesco, Fiumara, Giacomo, De Meo, Pasquale, Bagdasar, Ovidiu, Song, Wei, Liotta, Antonio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8357088/
https://www.ncbi.nlm.nih.gov/pubmed/34379625
http://dx.doi.org/10.1371/journal.pone.0255067
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author Ficara, Annamaria
Cavallaro, Lucia
Curreri, Francesco
Fiumara, Giacomo
De Meo, Pasquale
Bagdasar, Ovidiu
Song, Wei
Liotta, Antonio
author_facet Ficara, Annamaria
Cavallaro, Lucia
Curreri, Francesco
Fiumara, Giacomo
De Meo, Pasquale
Bagdasar, Ovidiu
Song, Wei
Liotta, Antonio
author_sort Ficara, Annamaria
collection PubMed
description 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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spelling pubmed-83570882021-08-12 Criminal networks analysis in missing data scenarios through graph distances Ficara, Annamaria Cavallaro, Lucia Curreri, Francesco Fiumara, Giacomo De Meo, Pasquale Bagdasar, Ovidiu Song, Wei Liotta, Antonio PLoS One Research Article 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. Public Library of Science 2021-08-11 /pmc/articles/PMC8357088/ /pubmed/34379625 http://dx.doi.org/10.1371/journal.pone.0255067 Text en © 2021 Ficara et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Ficara, Annamaria
Cavallaro, Lucia
Curreri, Francesco
Fiumara, Giacomo
De Meo, Pasquale
Bagdasar, Ovidiu
Song, Wei
Liotta, Antonio
Criminal networks analysis in missing data scenarios through graph distances
title Criminal networks analysis in missing data scenarios through graph distances
title_full Criminal networks analysis in missing data scenarios through graph distances
title_fullStr Criminal networks analysis in missing data scenarios through graph distances
title_full_unstemmed Criminal networks analysis in missing data scenarios through graph distances
title_short Criminal networks analysis in missing data scenarios through graph distances
title_sort criminal networks analysis in missing data scenarios through graph distances
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8357088/
https://www.ncbi.nlm.nih.gov/pubmed/34379625
http://dx.doi.org/10.1371/journal.pone.0255067
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