Ranking influential nodes by combining normalized degree centrality and fine-grained K-Shell

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.