Information Entropy Based on Propagation Feature of Node for Identifying the Influential Nodes

For understanding and controlling spreading in complex networks, identifying the most influential nodes, which can be applied to disease control, viral marketing, air traffic control, and many other fields, is of great importance. By taking the effect of the spreading rate on information entropy int...

Full description

Saved in:
Bibliographic Details
Published in:Complexity (New York, N.Y.) N.Y.), 2021, Vol.2021 (1)
Main Authors: Zhong, Linfeng, Bai, Yu, Tian, Yan, Luo, Chen, Huang, Jin, Pan, Weijun
Format: Article
Language:English
Subjects:
Air traffic control
Benchmarks
Disease control
Empirical analysis
Entropy (Information theory)
Methods
Networks
Nodes
Performance evaluation
Propagation
Social networks
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:For understanding and controlling spreading in complex networks, identifying the most influential nodes, which can be applied to disease control, viral marketing, air traffic control, and many other fields, is of great importance. By taking the effect of the spreading rate on information entropy into account, we proposed an improved information entropy (IIE) method. Compared to the benchmark methods in the six different empirical networks, the IIE method has been found with a better performance on Kendall’s Tau and imprecision function under the Susceptible Infected Recovered (SIR) model. Especially in the Facebook network, Kendall’s Tau can grow by 120% as compared with the original IE method. And, there is also an equally good performance in the comparative analysis of imprecise functions. The imprecise functions’ value of the IIE method is smaller than the benchmark methods in six networks.
ISSN:1076-2787
1099-0526
DOI:10.1155/2021/5554322