Identifying influential nodes in complex networks: A node information dimension approach

被引：97
作者：
Bian, Tian [1 ,2 ]
Deng, Yong [1 ,2 ]
机构：
[1] Univ Elect Sci & Technol China, Inst Fundamental & Frontier Sci, Chengdu 610054, Sichuan, Peoples R China
[2] Southwest Univ, Sch Comp & Informat Sci, Chongqing 400715, Peoples R China
来源：
CHAOS | 2018年 / 28卷 / 04期
基金：
中国国家自然科学基金;
关键词：
SELF-SIMILARITY; WEB; CENTRALITY; ANATOMY;
D O I：
10.1063/1.5030894
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In the field of complex networks, how to identify influential nodes is a significant issue in analyzing the structure of a network. In the existing method proposed to identify influential nodes based on the local dimension, the global structure information in complex networks is not taken into consideration. In this paper, a node information dimension is proposed by synthesizing the local dimensions at different topological distance scales. A case study of the Netscience network is used to illustrate the efficiency and practicability of the proposed method. Published by AIP Publishing.
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页数：8
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