Identifying influential nodes in complex networks from global perspective

被引：96

作者：

Zhao, Jie ^{[1
]}

Wang, Yunchuan ^{[2
]}

Deng, Yong ^{[1
]}

机构：

[1] Univ Elect Sci & Technol China, Inst Fundamental & Frontier Sci, Chengdu 610054, Peoples R China

[2] Univ Elect Sci & Technol China, Sch Elect Sci & Engn, Chengdu 611731, Peoples R China

来源：

CHAOS SOLITONS & FRACTALS | 2020年 / 133卷

基金：

中国国家自然科学基金;

关键词：

Complex networks; Influential nodes; Global importance; Unweighted network; SI model; CENTRALITY MEASURE; LINK PREDICTION; SPREADERS; RANKING; IDENTIFICATION;

D O I：

10.1016/j.chaos.2020.109637

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

How to identify influential nodes in complex networks is an open issue. Several centrality measures have been proposed to address this. But these studies concentrate only on only one aspect. To solve this problem, a novel method to identify influential nodes is proposed, which takes into account not only the importance of itself but also the influence of all nodes in the graph into consideration. This approach has superiority in identifying nodes that seem unimportant but are important in the complex network. Besides, it provides a quantitative model to measure the global importance of each node (GIN). The comparison experiments conducted on six different networks illustrate the effectiveness of the proposed method. (C) 2020 Elsevier Ltd. All rights reserved.

引用

页数：12

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