Identifying Important Nodes by Entropy Method

There are some vital nodes in the networks that have a huge impact on the structure and function of them. The identification of key nodes is a hot topic in complex networks. In this paper, an entropy-based node importance evaluation algorithm (EnRank) is proposed. It uses degree to characterize the importance of the node itself. The Attraction Rate and Transfer Rate of the node are used to characterize the relative importance of a node to its neighboring nodes obliquely. Then, we calculate the comprehensive index of nodes by entropy method. It will consider both the importance of itself and the relative importance of nodes to their neighbors, which makes the results more accurate. The EnRank algorithm is applied to ARPA network, social network and aviation network, the experimental results show that the algorithm can distinguish the differences among nodes more effectively. Finally, the simulation experiments of cascading failure further prove the effectiveness of our method.