  We study a social learning scheme where at every time instant, each agent
chooses to receive information from one of its neighbors at random. We show
that under this sparser communication scheme, the agents learn the truth
eventually and the asymptotic convergence rate remains the same as the standard
algorithms which use more communication resources. We also derive large
deviation estimates of the log-belief ratios for a special case where each
agent replaces its belief with that of the chosen neighbor.

Multiagent Systems;Social and Information Networks;Signal Processing