Identifying Influential Spreaders in Complex Networks by Considering the Impact of the Number of Shortest Paths  

作　　者：LUAN Yangyang BAO Zhongkui ZHANG Haifeng 

机构地区：[1]School of Mathematical Science,Anhui University,Hefei 230601,China

出　　处：《Journal of Systems Science & Complexity》2021年第6期2168-2181,共14页系统科学与复杂性学报（英文版）

基　　金：supported by the National Natural Science Foundation of China under Grant Nos.61973001,61806001;the Natural Science Foundation of Anhui Province under Grant No.1808085MF201;the State Key Laboratory for Ocean Big Data Mining and Application of Zhejiang Province under Grant No.OBDMA201502;Anhui University Foundation under Grant No.01005102。

摘　　要：The study on how to identify influential spreaders in complex networks is becoming increasingly significant.Previous studies demonstrate that considering the shortest path length can improve the accuracy of identification,but which ignore the influence of the number of shortest paths.In many cases,even though the shortest path length of two nodes is rather larger,their interaction influence is also significant if the number of shortest paths between them is considerable.Inspired by this fact,the authors propose an improved centrality index(ICC)based on well-known closeness centrality and a semi-local iterative algorithm(semi-IA)to study the impact of the number of shortest paths on the identification of the influential spreaders.By comparing with several traditional centrality indices,such as degree centrality,k-shell decomposition,betweenness centrality and eigenvector centrality,the experimental results on real networks indicate that the ICC index and semi-IA have a better performance,regardless of the identification capability or the resolution.

关 键 词：Complex networks influential spreader number of shortest paths 

分 类 号：O157.5[理学—数学]