Zero-inflated stochastic block modelling of efficiency-security trade-offs in weighted criminal networks

Abstract

Criminal networks arise from the attempt to balance a need of establishing frequent ties among affiliates to facilitate coordination of illegal activities, with the necessity to sparsify the overall connectivity architecture to hide from law enforcement. This efficiency-security trade-off is also combined with the creation of groups of redundant criminals that exhibit similar connectivity patterns, thus guaranteeing resilient network architectures. State-of-the-art models for such data are not designed to infer these unique structures. In contrast to such solutions, we develop a tractable Bayesian zero-inflated Poisson stochastic block model (ZIP–SBM), which identifies groups of redundant criminals having similar connectivity patterns, and infers both overt and covert block interactions within and across these groups. This is accomplished by modelling the weighted ties (corresponding to counts of interactions among pairs of criminals) via zero-inflated Poisson distributions with block-specific parameters that quantify complex patterns in the excess of zero ties in each block (security) relative to the distribution of the observed weighted ties within that block (efficiency). The performance of ZIP–SBM is illustrated in simulations and in a study of summit co-attendances in a complex Mafia organization, where we unveil efficiency-security structures adopted by the criminal organization that were hidden to previous analyses.