Identifying vital nodes in complex networks based on information entropy, minimum dominating set and distance

Abstract

Identifying the vital nodes in complex networks is essential in disease transmission control and network attack protection. In this paper, in order to identify the vital nodes, we define a centrality method named EMDC, which is based on information entropy, minimum dominating set (MDS) and the distance between node pairs. This method calculates the local spreading capability (LSC) of node by information entropy and selects that nodes have the largest value of LSC as core nodes by MDS. Then it defines the node’s spreading capability (SC) to use the sum of weighted distances from a node to the core nodes. Finally, the nodes are ranked by considering SC of their neighbors. The key nodes can be further identified in complex networks. In order to verify the effectiveness of this method, key nodes identification simulation experiments are carried out on 11 real networks, Scale-Free (BA) networks and Small-World (WS) networks, respectively. Experimental results show that this method can more effectively identify the influence of nodes in the networks.