Bayesian modeling of criminal networks

Daniele Durante, Bocconi University

Abstract: Europol recently defined criminal networks as a modern version of the Hydra mythological creature, with covert and complex structure. Indeed, connectivity data among criminals are subject to measurement errors, structured missingness patterns, and exhibit a sophisticated combination of an unknown number of core-periphery, assortative and disassortative structures that may encode key architectures of the criminal organization. The coexistence of these noisy, and possibly multiscale, block patterns limits the reliability of community detection algorithms routinely-used in criminology, thereby leading to overly-simplified and possibly biased reconstructions of organized crime architectures. In this talk, I will present a number of Bayesian model-based solutions which aim at covering these gaps via a combination of stochastic block models and priors for random partitions arising from Bayesian nonparametrics. These include Gibbs-type priors, and random partition priors driven by the urn scheme of a hierarchical normalized completely random measure. Product-partition models to incorporate criminals’ attributes, and zero-inflated Poisson representations accounting for weighted edges and security strategies, will be also discussed. Collapsed Gibbs samplers for posterior computation are presented, and refined strategies for estimation, prediction, uncertainty quantification and model selection will be outlined. Results are illustrated in an application to an Italian Mafia network, where the proposed models unveil a structure of the criminal organization mostly hidden to state-of-the-art alternatives routinely used in criminology. I will conclude the talk by introducing an innovative phylogenetic latent space model that learns nested modular hierarchies among criminals from the formation process of the corresponding latent connectivity features.