Identifying vital nodes in complex networks by adjacency information entropy

被引:57
|
作者
Xu, Xiang [1 ]
Zhu, Cheng [1 ]
Wang, Qingyong [1 ]
Zhu, Xianqiang [1 ]
Zhou, Yun [1 ]
机构
[1] Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha 410072, Peoples R China
基金
中国国家自然科学基金;
关键词
INFLUENTIAL NODES; CENTRALITY; IDENTIFICATION; SPREADERS;
D O I
10.1038/s41598-020-59616-w
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Identifying the vital nodes in networks is of great significance for understanding the function of nodes and the nature of networks. Many centrality indices, such as betweenness centrality (BC), eccentricity centrality (EC), closeness centricity (CC), structural holes (SH), degree centrality (DC), PageRank (PR) and eigenvector centrality (VC), have been proposed to identify the influential nodes of networks. However, some of these indices have limited application scopes. EC and CC are generally only applicable to undirected networks, while PR and VC are generally used for directed networks. To design a more applicable centrality measure, two vital node identification algorithms based on node adjacency information entropy are proposed in this paper. To validate the effectiveness and applicability of the proposed algorithms, contrast experiments are conducted with the BC, EC, CC, SH, DC, PR and VC indices in different kinds of networks. The results show that the index in this paper has a high correlation with the local metric DC, and it also has a certain correlation with the PR andVC indices for directed networks. In addition, the experimental results indicate that our algorithms can effectively identify the vital nodes in different networks.
引用
收藏
页数:12
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