How to identify influential nodes in complex networks is an essential issue in the study of network characteristics.A number of methods have been proposed to address this problem,but most of them focus on only one asp...How to identify influential nodes in complex networks is an essential issue in the study of network characteristics.A number of methods have been proposed to address this problem,but most of them focus on only one aspect.Based on the gravity model,a novel method is proposed for identifying influential nodes in terms of the local topology and the global location.This method comprehensively examines the structural hole characteristics and K-shell centrality of nodes,replaces the shortest distance with a probabilistically motivated effective distance,and fully considers the influence of nodes and their neighbors from the aspect of gravity.On eight real-world networks from different fields,the monotonicity index,susceptible-infected-recovered(SIR)model,and Kendall’s tau coefficient are used as evaluation criteria to evaluate the performance of the proposed method compared with several existing methods.The experimental results show that the proposed method is more efficient and accurate in identifying the influence of nodes and can significantly discriminate the influence of different nodes.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61663030 and 61663032)。
文摘How to identify influential nodes in complex networks is an essential issue in the study of network characteristics.A number of methods have been proposed to address this problem,but most of them focus on only one aspect.Based on the gravity model,a novel method is proposed for identifying influential nodes in terms of the local topology and the global location.This method comprehensively examines the structural hole characteristics and K-shell centrality of nodes,replaces the shortest distance with a probabilistically motivated effective distance,and fully considers the influence of nodes and their neighbors from the aspect of gravity.On eight real-world networks from different fields,the monotonicity index,susceptible-infected-recovered(SIR)model,and Kendall’s tau coefficient are used as evaluation criteria to evaluate the performance of the proposed method compared with several existing methods.The experimental results show that the proposed method is more efficient and accurate in identifying the influence of nodes and can significantly discriminate the influence of different nodes.
