Criminal networks analysis in missing data scenarios through graph distances

• Published in: PloS one Vol. 16; no. 8; p. e0255067
• Main Authors: Ficara, Annamaria, Cavallaro, Lucia, Curreri, Francesco, Fiumara, Giacomo, De Meo, Pasquale, Bagdasar, Ovidiu, Song, Wei, Liotta, Antonio
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 11-08-2021; Public Library of Science (PLoS)
• Subjects: Analysis; Biology and Life Sciences; Computer and Information Sciences; Crime; Crime prevention; Criminal investigations; Data collection; Data entry; Enforcement; Engineering and Technology; Euclidean geometry; Evidence; Information management; Law enforcement; Methods; Missing data; Networks; Organizations; Organized crime; People and Places; Physical Sciences; Social network analysis; Social Sciences; Software; Statistical analysis; Supervision; Surveillance; Terrorist organizations; United States; United Kingdom > UK; Italy
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0255067

Data 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.