Identifying influential nodes is one of the crucial issues for controlling the network propagation process and exploring network properties in complex networks. Nevertheless, the accuracy of existing methods is still a challenge. In this paper, we rank influential nodes by considering tow aspects. On one hand, a normalized degree centrality is proposed to measure the local influence of each node. On the other hand, an improved fine-grained K-Shell decomposition is defined to measure the spreading ability of neighbors of a node. Further, a novel ranking measure is proposed by combining the normalized degree centrality and fine-grained K-Shell (NDF-FKS). The Susceptible-Infected-Recovery (SIR) model is used to simulate the network propagation process. Experiments with the model are performed on eight synthetic networks and four real networks. The NDF-FKS compared with six measures for accuracy and resolution. The results show that the accuracy of NDF-FKS outperforms existing six measures and has a competitive performance on distinguishing influential nodes.
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