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

Berlusconi, Giulia, Calderoni, Francesco, Parolini, Nicola, Verani, Marco and Piccardi, Carlo (2017) Link Prediction in Criminal Networks: A Tool for Criminal Intelligence Analysis PLoS ONE, 11 (4), e0154244.

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Abstract

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.

Item Type: Article
Divisions : Faculty of Arts and Social Sciences > Department of Sociology
Authors :
NameEmailORCID
Berlusconi, Giuliag.berlusconi@surrey.ac.uk
Calderoni, Francesco
Parolini, Nicola
Verani, Marco
Piccardi, Carlo
Date : 22 April 2017
Identification Number : 10.1371/ journal.pone.0154244
Copyright Disclaimer : © 2016 Berlusconi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Depositing User : Melanie Hughes
Date Deposited : 06 Dec 2017 11:48
Last Modified : 06 Dec 2017 11:48
URI: http://epubs.surrey.ac.uk/id/eprint/845122

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