  We address the problem of learning the legitimacy of other agents in a
multiagent network when an unknown subset is comprised of malicious actors. We
specifically derive results for the case of directed graphs and where
stochastic side information, or observations of trust, is available. We refer
to this as ``learning trust'' since agents must identify which neighbors in the
network are reliable, and we derive a protocol to achieve this. We also provide
analytical results showing that under this protocol i) agents can learn the
legitimacy of all other agents almost surely, and that ii) the opinions of the
agents converge in mean to the true legitimacy of all other agents in the
network. Lastly, we provide numerical studies showing that our convergence
results hold in practice for various network topologies and variations in the
number of malicious agents in the network.
