Abstract: The ranking of nodes’ influence has always been a hot issue in the research area of complex networks.Susceptible-infected-recovered(SIR) model is an ideal nodes’ influence ranking method,which is commonly used to evaluate other nodes’ in-fluence ranking methods.But it is difficult to be applied in practice due to its high time complexity.This paper proposes a nodes’ influence ranking model based on sir value learning.Both the local structure and global structure information of nodes are used as features in the model.The sir value learning model is constructed by means of a deep learning model,which is trained on nodes’ features and sir data set in synthetic graphs with the same size.The trained model can predict sir value based on nodes’ features,and then rank nodes’ influence based on predicted sir.In this paper,a specific nodes’ influence ranking method is implemented based on the proposed model,and experiments are carried out on five real networks to verify the effectiveness of the method.The results show that the accuracy and monotonicity of nodes’ influence ranking results are improved compared with degree centrality,Kshell and Weighted Kshell degree neighborhood.

Key words: Complex networks, Nodes’ influence, SIR, Influence ranking