The identification of influential nodes based on structure similarity

The identification of influential nodes in complex networks is an open issue. To address it, many centrality measures have been proposed, among which the most representative iteration algorithm is the PageRank algorithm. However, it ignores the correlation between nodes and assumes that the jumping probability from a node to its adjacent nodes is the same. To make up it, we proposed a method to improve the PageRank based on the structural similarity of nodes calculated by Kullback-Leibler divergence. The Susceptible-infected (SI) model was used in six real networks, and the results of comparison experiments demonstrate the effectiveness of the proposed method.