A mechanics model based on information entropy for identifying influencers in complex networks

Area of Science:

• Network Science
• Information Theory
• Computational Social Science

Background:

• Complex networks exhibit properties like scale-free and self-organization.
• Identifying significant spreaders (influencers) is crucial for network security and understanding propagation.
• Existing methods struggle to effectively integrate local and global network information.

Purpose of the Study:

• To propose a novel method for discovering crucial spreaders in complex networks.
• To address the challenge of combining local and global information for influencer identification.
• To improve the accuracy and efficiency of identifying key nodes in network structures.

Main Methods:

• A generalized mechanics model is enhanced using information entropy.
• Local information influence is quantified via information entropy of neighbor nodes.
• Global information interaction is assessed by calculating shortest path distances between nodes.

Main Results:

• The proposed information entropy-based generalized mechanics model accurately identifies crucial spreaders.
• The approach demonstrates superior speed and precision compared to traditional methods and state-of-the-art benchmarks.
• Extensive tests on eleven real-world networks validate the model's effectiveness.

Conclusions:

• The developed model offers an effective solution for identifying influencers in complex networks.
• Combining local (information entropy) and global (shortest distance) metrics enhances spreader identification.
• This approach provides significant practical value for network security and propagation control.