OPINION DYNAMICS APPROACH FOR IDENTIFYING COMMUNITY STRUCTURES IN COMPLEX NETWORKS

This paper suggests an opinion dynamics approach to define community structures in complex networks. If a typical opinion dynamics model is applied to a network with a community structure, the network can separate in two groups of nodes. Such bisection in a given network can arise in many different ways depending on the initial conditions. The opinion distance between two nodes is defined as the probability of disagreement, or the probability that the two nodes belong to different bisections in multiple Monte Carlo simulations. The communities can be defined in terms of the distance. Closer nodes belong to the same community. Three opinion dynamics models were tested to show how the method works. Through various example networks, it was shown that the distance data can be used as a unique metric for identifying hierarchical structures and overlapping nodes in networks, as well as for identifying the community structure itself.