An improved gravity model to identify influential nodes in complex networks based on k-shell method

被引:86
|
作者
Yang, Xuan [1 ]
Xiao, Fuyuan [1 ]
机构
[1] Southwest Univ, Sch Comp & Informat Sci, Chongqing 400715, Peoples R China
基金
中国国家自然科学基金;
关键词
Complex networks; Influential nodes; Gravity model; K-shell; IDENTIFICATION; CENTRALITY; RANKING; SPREADERS; USERS;
D O I
10.1016/j.knosys.2021.107198
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
To find the important nodes in complex networks is a fundamental issue. A number of methods have been recently proposed to address this problem but most previous studies have the limitations, and few of them considering both local and global information of the network. The location of node, which is a significant property of a node in the network, is seldom considered in identifying the importance of nodes before. To address this issue, we propose an improved gravity centrality measure on the basis of the k-shell algorithm named KSGC to identify influential nodes in the complex networks. Our method takes the location of nodes into consideration, which is more reasonable compared to original gravity centrality measure. Several experiments on real-world networks are conducted to show that our method can effectively evaluate the importance of nodes in complex networks. (C) 2021 Elsevier B.V. All rights reserved.
引用
收藏
页数:11
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