By Wang Ngai Yeung, Riccardo Di Clemente & Renaud Lambiotte
Criminal networks such as human trafficking rings are threats to the rule of law, democracy and public safety in our global society. Network science provides invaluable tools to identify key players and design interventions for Law Enforcement Agencies (LEAs), e.g., to dismantle their organisation. However, poor data quality and the robustness of criminal networks make effective intervention extremely challenging. Although there exists a large body of work building and applying network scientific tools to green intervene criminal networks, these work often neglect the problems of data incompleteness and inaccuracy. Moreover, there is thus far no comprehensive understanding of the impacts of data quality on the downstream effectiveness of interventions. This work investigates the relationship between data quality and intervention effectiveness based on classical graph theoretic and machine learning-based targeting approaches. Decentralization emerges as a major factor in network robustness, particularly under conditions of incomplete data, which renders intervention strategies largely ineffective. Moreover, the robustness of centralized networks can be boosted using simple heuristics, making targeted intervention more infeasible. Consequently, we advocate for a more cautious application of network science in disrupting criminal networks, the continuous development of an interoperable intelligence ecosystem, and the creation of novel network inference techniques to address data quality challenges.
EPJ Data Sci. 14, 37 (2025)
