%0 Journal Article
%T Bayesian Discovery of Threat Networks
%A Steven T. Smith
%A Edward K. Kao
%A Kenneth D. Senne
%A Garrett Bernstein
%A Scott Philips
%J Computer Science
%D 2013
%I arXiv
%R 10.1109/TSP.2014.2336613
%X A novel unified Bayesian framework for network detection is developed, under which a detection algorithm is derived based on random walks on graphs. The algorithm detects threat networks using partial observations of their activity, and is proved to be optimum in the Neyman-Pearson sense. The algorithm is defined by a graph, at least one observation, and a diffusion model for threat. A link to well-known spectral detection methods is provided, and the equivalence of the random walk and harmonic solutions to the Bayesian formulation is proven. A general diffusion model is introduced that utilizes spatio-temporal relationships between vertices, and is used for a specific space-time formulation that leads to significant performance improvements on coordinated covert networks. This performance is demonstrated using a new hybrid mixed-membership blockmodel introduced to simulate random covert networks with realistic properties.
%U http://arxiv.org/abs/1311.5552v3