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:	Annamaria Ficara, Lucia Cavallaro, Francesco Curreri, Giacomo Fiumara, Pasquale De Meo, Ovidiu Bagdasar, Wei Song, Antonio Liotta
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f912ea94763b4c5e908d3458a1ff4f37
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spelling	oai:doaj.org-article:f912ea94763b4c5e908d3458a1ff4f372021-12-02T20:15:05ZCriminal networks analysis in missing data scenarios through graph distances.1932-620310.1371/journal.pone.0255067https://doaj.org/article/f912ea94763b4c5e908d3458a1ff4f372021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255067https://doaj.org/toc/1932-6203Data 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.Annamaria FicaraLucia CavallaroFrancesco CurreriGiacomo FiumaraPasquale De MeoOvidiu BagdasarWei SongAntonio LiottaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0255067 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Annamaria Ficara Lucia Cavallaro Francesco Curreri Giacomo Fiumara Pasquale De Meo Ovidiu Bagdasar Wei Song Antonio Liotta Criminal networks analysis in missing data scenarios through graph distances.
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.
format	article
author	Annamaria Ficara Lucia Cavallaro Francesco Curreri Giacomo Fiumara Pasquale De Meo Ovidiu Bagdasar Wei Song Antonio Liotta
author_facet	Annamaria Ficara Lucia Cavallaro Francesco Curreri Giacomo Fiumara Pasquale De Meo Ovidiu Bagdasar Wei Song Antonio Liotta
author_sort	Annamaria Ficara
title	Criminal networks analysis in missing data scenarios through graph distances.
title_short	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_sort	criminal networks analysis in missing data scenarios through graph distances.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/f912ea94763b4c5e908d3458a1ff4f37
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_version_	1718374622227857408

Criminal networks analysis in missing data scenarios through graph distances.

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