Social learning on networks with community structure

Abstract

Almost all existing social learning models assume that there is only one type of agents in the society in order to avoid identification problem. In this work, we assume that there are various types of agents according to the communities they locate in. We design the rule of weight adjustment and testify that the updating rule with weight adjustment ensures learning on the whole social network. Furthermore, we show that how convergence speed is influenced by two updating-relevant parameters, and present instruction on how to attain the optimal social learning efficiency.