A hybrid influence method based on information entropy to identify the key nodes

Frontiers in Physics

Physics

Liang Zhao¹  Lei Zhang²  Hao Yang²  Weijun Pan²  Xiangying Gao²  Jin Huang²  Pengfei Chen²  Linfeng Zhong³ 

[1] Operation Management Center of ATMB, Civil Aviation Administration of China, Beijing, China;School of Air Traffic Management, Civil Aviation Flight University of China, Guanghan, China;School of Air Traffic Management, Civil Aviation Flight University of China, Guanghan, China;Chengdu GoldTel Industry Group Co., Ltd., Chengdu, China;

关键词: complex network;    key nodes;    information entropy;    epidemic threshold;    SIR;   

DOI  :  10.3389/fphy.2023.1280537

 received in 2023-08-20, accepted in 2023-09-11,  发布年份 2023

来源: Frontiers

【 摘 要 】

Identifying the key nodes in complicated networks is an essential topic. A number of methods have been developed in recent years to solve this issue more effectively. Multi-attribute ranking is a widely used and efficient method to increase the accuracy of identifying the key nodes. Using k-shell iteration information and propagation threshold differences, we thoroughly analyze the node’s position attribute and the propagation attribute to offer a hybrid influence method based on information entropy. The two attributes will be weighted using the information entropy weighting method, and then the nodes’ influence ranking will be calculated. Correlation experiments in nine different networks were carried out based on the Susceptible–Infected–Recovered (SIR) model. Among these, we use the imprecision function, Kendall’s correlation coefficient, and the complementary cumulative distribution function to validate the suggested method. The experimental results demonstrate that our suggested method outperforms previous node ranking methods in terms of monotonicity, relevance, and accuracy and performs well to achieve a more accurate ranking of nodes in the network.

【 授权许可】

Unknown   
Copyright © 2023 Zhong, Gao, Zhao, Zhang, Chen, Yang, Huang and Pan.