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dc.contributor.authorFicara, Annamaria
dc.contributor.authorCavallaro, Lucia
dc.contributor.authorCurreri, Francesco
dc.contributor.authorFiumara, Giacomo
dc.contributor.authorDe Meo, Pasquale
dc.contributor.authorBagdasar, Ovidiu
dc.contributor.authorSong, Wei
dc.contributor.authorLiotta, Antonio
dc.date.accessioned2021-09-14T14:00:28Z
dc.date.available2021-09-14T14:00:28Z
dc.date.issued2021-08-11
dc.identifier.citationFicara, A., Cavallaro, L., Curreri, F., Fiumara, G., De Meo, P., Bagdasar, O., Song, W. and Liotta, A., (2021). 'Criminal Networks Analysis in Missing Data scenarios through Graph Distances'. PLoS ONE, 16(8), pp. 1-18.en_US
dc.identifier.doi10.1371/journal.pone.0255067
dc.identifier.urihttp://hdl.handle.net/10545/625996
dc.description.abstractData 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.en_US
dc.description.sponsorshipLibera Università di Bolzanoen_US
dc.language.isoenen_US
dc.publisherPublic Library of Science (PLoS)en_US
dc.relation.urlhttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0255067en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectMultidisciplinaryen_US
dc.subjectPoliceen_US
dc.subjectRoadsen_US
dc.subjectCrimeen_US
dc.subjectEigenvaluesen_US
dc.titleCriminal networks analysis in missing data scenarios through graph distancesen_US
dc.typeArticleen_US
dc.identifier.eissn1932-6203
dc.contributor.departmentUniversity of Palermo, Palermo, Italyen_US
dc.contributor.departmentUniversity of Messina, Messina, Italyen_US
dc.identifier.journalPLoS ONEen_US
dc.source.journaltitlePLOS ONE
dc.source.volume16
dc.source.issue8
dc.source.beginpagee0255067
dcterms.dateAccepted2021-07-08
refterms.dateFOA2021-09-14T14:00:28Z
dc.author.detail782275en_US


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