A new method to identify influential nodes based on relative entropy

How to identify influential nodes is still an open and vital issue in complex networks. To address this problem, a lot of centrality measures have been developed, however, only single measure is focused on by the existing studies, which has its own shortcomings. In this paper, a novel method is proposed to identify influential nodes using relative entropy and TOPSIS method, which combines the advantages of existing centrality measures. Because information flow spreads in different ways in different networks. In the specific network, the appropriate centrality measures should be considered to sort the nodes. In addition, the remoteness between the alternative and the positive/negetive ideal solution is redefined based on relative entropy, which is proven to be more effective in TOPSIS method. To demonstrate the effectiveness of the proposed method, four real networks are selected to conduct several experiments for identifying influential nodes, and the advantages of the method can be illustrated based on the experimental results.