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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
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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. |
format | Online Article Text |
id | pubmed-4841537 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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