Role extraction in complex networks and its application in control of networks

Given a large network, dynamics of the network are determined by both nodes' features and network connections. Some features could be extracted from node labels and other kinds of priori knowledge. But how to perform the feature classification without priori knowledge is a challenge. This paper addresses the key problem: how do we conduct role extraction in networks with only edge connections known? On the basis of behavior differences in dynamics, nodes are classified into three role groups: Leaders(L), Communicators(C) and Members(M). Unlike traditional community detections, we detect overlapping communities by link clustering first and then classify nodes according to the community entropy, which describes the disorder of how many different communities a node connects to. We propose a time saving and unsupervised learning approach for automatically discovering nodes' roles based solely on network topology. The effectiveness of this method is demonstrated on six real-world networks through pinning control. By controlling communicator nodes, the controllability is enhanced and the cost for control is reduced obviously in networks with strong community structure.