Strategic node identification in complex network dynamics

Abstract

Detecting significant nodes in intricate networks is essential for various purposes, including market advertising, rumor management, and predicting scientific publications. Existing algorithms, from basic degree methods to more complex approaches, have been developed, but there is a need for a more robust solution. Traditional methods often focus on local network details, neglecting global aspects. This study introduces a network structure entropy-based node importance ranking method that considers both local and global information. The method’s efficacy is validated through comparisons with three benchmarks, showcasing strong performance on two real-world datasets. Further work could explore scalability and applicability in dynamic scenarios.