  Non-Bayesian social learning is a framework for distributed hypothesis
testing aimed at learning the true state of the environment. Traditionally, the
agents are assumed to receive observations conditioned on the same true state,
although it is also possible to examine the case of heterogeneous models across
the graph. One important special case is when heterogeneity is caused by the
presence of malicious agents whose goal is to move the agents toward a wrong
hypothesis. In this work, we propose an algorithm that allows discovering the
true state of every individual agent based on the sequence of their beliefs. In
so doing, the methodology is also able to locate malicious behavior.
