A Bayesian semiparametric model for terrorist networks

In this work we focus on the “Noordin Mohamed Top” dataset, develop- ing an asymmetric approach that treats one network as response and the remaining as covariates. The objective is to identify which information may be useful in pre- dicting terrorists’ collaboration in a bombing attack, identifying at the same time the most influential subjects involved in these dynamics. Such aim is addressed through an asymmetric Bayesian semi-parametric model for networks that, through a suitable prior specification, integrates a flexible regularization and the detection of leading nodes