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Link Prediction in Criminal Networks: A Tool for Criminal Intelligence Analysis

The problem of link prediction has recently received increasing attention from scholars in network science. In social network analysis, one of its aims is to recover missing links, namely connections among actors which are likely to exist but have not been reported because data are incomplete or sub...

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Autores principales: Berlusconi, Giulia, Calderoni, Francesco, Parolini, Nicola, Verani, Marco, Piccardi, Carlo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4841537/
https://www.ncbi.nlm.nih.gov/pubmed/27104948
http://dx.doi.org/10.1371/journal.pone.0154244
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author Berlusconi, Giulia
Calderoni, Francesco
Parolini, Nicola
Verani, Marco
Piccardi, Carlo
author_facet Berlusconi, Giulia
Calderoni, Francesco
Parolini, Nicola
Verani, Marco
Piccardi, Carlo
author_sort Berlusconi, Giulia
collection PubMed
description The problem of link prediction has recently received increasing attention from scholars in network science. In social network analysis, one of its aims is to recover missing links, namely connections among actors which are likely to exist but have not been reported because data are incomplete or subject to various types of uncertainty. In the field of criminal investigations, problems of incomplete information are encountered almost by definition, given the obvious anti-detection strategies set up by criminals and the limited investigative resources. In this paper, we work on a specific dataset obtained from a real investigation, and we propose a strategy to identify missing links in a criminal network on the basis of the topological analysis of the links classified as marginal, i.e. removed during the investigation procedure. The main assumption is that missing links should have opposite features with respect to marginal ones. Measures of node similarity turn out to provide the best characterization in this sense. The inspection of the judicial source documents confirms that the predicted links, in most instances, do relate actors with large likelihood of co-participation in illicit activities.
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spelling pubmed-48415372016-04-29 Link Prediction in Criminal Networks: A Tool for Criminal Intelligence Analysis Berlusconi, Giulia Calderoni, Francesco Parolini, Nicola Verani, Marco Piccardi, Carlo PLoS One Research Article The problem of link prediction has recently received increasing attention from scholars in network science. In social network analysis, one of its aims is to recover missing links, namely connections among actors which are likely to exist but have not been reported because data are incomplete or subject to various types of uncertainty. In the field of criminal investigations, problems of incomplete information are encountered almost by definition, given the obvious anti-detection strategies set up by criminals and the limited investigative resources. In this paper, we work on a specific dataset obtained from a real investigation, and we propose a strategy to identify missing links in a criminal network on the basis of the topological analysis of the links classified as marginal, i.e. removed during the investigation procedure. The main assumption is that missing links should have opposite features with respect to marginal ones. Measures of node similarity turn out to provide the best characterization in this sense. The inspection of the judicial source documents confirms that the predicted links, in most instances, do relate actors with large likelihood of co-participation in illicit activities. Public Library of Science 2016-04-22 /pmc/articles/PMC4841537/ /pubmed/27104948 http://dx.doi.org/10.1371/journal.pone.0154244 Text en © 2016 Berlusconi et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://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
Berlusconi, Giulia
Calderoni, Francesco
Parolini, Nicola
Verani, Marco
Piccardi, Carlo
Link Prediction in Criminal Networks: A Tool for Criminal Intelligence Analysis
title Link Prediction in Criminal Networks: A Tool for Criminal Intelligence Analysis
title_full Link Prediction in Criminal Networks: A Tool for Criminal Intelligence Analysis
title_fullStr Link Prediction in Criminal Networks: A Tool for Criminal Intelligence Analysis
title_full_unstemmed Link Prediction in Criminal Networks: A Tool for Criminal Intelligence Analysis
title_short Link Prediction in Criminal Networks: A Tool for Criminal Intelligence Analysis
title_sort link prediction in criminal networks: a tool for criminal intelligence analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4841537/
https://www.ncbi.nlm.nih.gov/pubmed/27104948
http://dx.doi.org/10.1371/journal.pone.0154244
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