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.

复杂网络中有影响力的节点的识别是一个未解决的问题。为了解决这个问题，已经提出了许多集中度度量，其中最具代表性的迭代算法是PageRank算法。但是，它忽略了节点之间的相关性，并假设从一个节点到其相邻节点的跳跃概率相同。为了弥补这一点，我们提出了一种基于Kullback-Leibler散度计算出的节点的结构相似性来改进PageRank的方法。在六个实际网络中使用了易感性（SI）模型，比较实验的结果证明了该方法的有效性。