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

• Published in: PloS one Vol. 11; no. 4; p. e0154244
• Main Authors: Berlusconi, Giulia, Calderoni, Francesco, Parolini, Nicola, Verani, Marco, Piccardi, Carlo
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 22.04.2016; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Biology and Life Sciences; Computer and Information Sciences; Crime; Crime - prevention & control; Criminal investigation; Criminal investigations; Criminals - statistics & numerical data; Criminology; Data collection; Datasets; Engineering and Technology; Humans; Identification; Information Storage and Retrieval - methods; Information Storage and Retrieval - statistics & numerical data; Inspection; Intelligence; Intelligence (information); Law enforcement; Links; Medicine and Health Sciences; Methods; Models, Statistical; Murders & murder attempts; Network analysis; Organized crime; People and Places; Physical Sciences; Predictions; Reproducibility of Results; Social change; Social network analysis; Social Networking; Social networks; Social organization; Social Sciences; Verbal communication; United States > US; Italy
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0154244

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.