A novel weight neighborhood centrality algorithm for identifying influential spreaders in complex networks

Identifying the most influential spreaders in complex networks is crucial for optimally using the network structure and designing efficient strategies to accelerate information dissemination or prevent epidemic outbreaks. In this paper, by taking into account the centrality of a node and its neighbors' centrality which depends on the diffusion importance of links, we propose a novel influence measure, the weight neighborhood centrality, to quantify the spreading ability of nodes in complex networks. To evaluate the performance of our method, we use the Susceptible-Infected-Recovered (SIR) model to simulate the epidemic spreading process on six real-world networks and four artificial networks. By measuring the rank imprecision and the rank correlation between the rank lists generated by simulation results via SIR and the ones generated by centrality measures, it shows that in general the weight neighborhood centrality can rank the spreading ability of nodes more accurately than its benchmark centrality, especially when using the degree k or coreness ks as the benchmark centrality. Further, we compare the monotonicity and the computational complexity of different ranking methods, which show that our method not only can be better at distinguishing the spreading ability of nodes but also can be used in large-scale networks due to the high computation efficiency.