Determining the impact of unknown individuals in criminality using network analysis of DNA matches. (February 2022) - Lavergne, Léo; Boivin, Rémi; Baechler, Simon; Jeuniaux, Patrick; Fiola, Karine; Séguin, Diane; Lefebvre, Jean-François; Milot, Emmanuel
Highlights: Forensic DNA matches provide a 18-year long picture of a criminal network. By betraying the presence of offenders unknown to the police, DNA traces address a lingering missing data issue. Social network metrics show that unknown individuals are as well integrated in the criminal network as known offenders. Analysing DNA matches with a social network approach can generate new intelligence for criminal investigations. Abstract: Criminal offenders missing from police files limit the capacity to reconstruct criminal networks for criminological research and operational purposes. Recent studies show that forensic DNA databanks offer potential to address this problem, through large-scale analysis of DNA matches, many of which involve unidentified offenders. Applying social network analysis (SNA) to 18 years of DNA match data from Québec, Canada, we found that 1400 unknowns do not occupy more marginal positions in the network than 13, 000 known offenders, and explain up to 18% of SNA values (e.g., betweenness centrality) for the latter while supporting 46% of their clustering values. Our results contrast with previous studies, showing moreover that unknown individuals who are positioned centrally in a network may have a larger impact than previously expected on investigation policing with implications for forensic intelligence.