The identification of influential nodes based on structure similarity

被引：33

作者：

Zhao, Jie ^{[1
]}

Song, Yutong ^{[1
]}

Liu, Fan ^{[1
]}

Deng, Yong ^{[1
,2
]}

机构：

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

[2] Shannxi Normal Univ, Sch Educ, Xian 710062, Shaanxi, Peoples R China

来源：

CONNECTION SCIENCE | 2021年 / 33卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Complex networks; influential nodes; PageRank; K-L divergence; SI model; COMPLEX NETWORKS; SMART CARD; SPREADERS; CENTRALITY;

D O I：

10.1080/09540091.2020.1806203

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The identification of influential nodes in complex networks is an open issue. To address it, many centrality measures have been proposed, among which the most representative iteration algorithm is the PageRank algorithm. However, it ignores the correlation between nodes and assumes that the jumping probability from a node to its adjacent nodes is the same. To make up it, we proposed a method to improve the PageRank based on the structural similarity of nodes calculated by Kullback-Leibler divergence. The Susceptible-infected (SI) model was used in six real networks, and the results of comparison experiments demonstrate the effectiveness of the proposed method.

引用

页码：201 / 218

页数：18

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