Identifying influential spreaders in complex networks based on entropy weight method and gravity law

作     者：Xiao-Li Yan Ya-Peng Cui Shun-Jiang Ni 闫小丽;崔亚鹏;倪顺江

作者机构：Institute of Public Safety ResearchTsinghua UniversityBeijing 100084China Department of Engineering PhysicsTsinghua UniversityBeijing 100084China Beijing Key Laboratory of City Integrated Emergency Response ScienceBeijing 100084China 

出 版 物：《Chinese Physics B》 (中国物理B（英文版）)

年 卷 期：2020年第29卷第4期

页      面：582-590页

核心收录：

学科分类：07[理学] 070104[理学-应用数学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0704[理学-天文学] 0701[理学-数学] 

基　　金：Project support by the National Key Research and Development Program of China(Grant No.2018YFF0301000) the National Natural Science Foundation of China(Grant Nos.71673161 and 71790613) 

主　　题：complex networks influential nodes entropy weight method gravity law 

摘      要：In complex networks,identifying influential spreader is of great significance for improving the reliability of networks and ensuring the safe and effective operation of ***,it is widely used in power networks,aviation networks,computer networks,and social networks,and so *** centrality methods mainly include degree centrality,closeness centrality,betweenness centrality,eigenvector centrality,k-shell,***,single centrality method is onesided and inaccurate,and sometimes many nodes have the same centrality value,namely the same ranking result,which makes it difficult to distinguish between *** to several classical methods of identifying influential nodes,in this paper we propose a novel method that is more full-scaled and universally *** into account in this method are several aspects of node’s properties,including local topological characteristics,central location of nodes,propagation characteristics,and properties of neighbor *** view of the idea of the multi-attribute decision-making,we regard the basic centrality method as node’s attribute and use the entropy weight method to weigh different attributes,and obtain node’s combined ***,the combined centrality is applied to the gravity law to comprehensively identify influential nodes in ***,the classical susceptible-infected-recovered(SIR)model is used to simulate the epidemic spreading in six real-society *** proposed method not only considers the four topological properties of nodes,but also emphasizes the influence of neighbor nodes from the aspect of *** is proved that the new method can effectively overcome the disadvantages of single centrality method and increase the accuracy of identifying influential nodes,which is of great significance for monitoring and controlling the complex networks.