Application of Grey Correlation Model to Key Spreader Identification in Multiplex Networks

Area of Science:

• Network Science
• Complex Systems Analysis
• Information Theory

Background:

• Multiplex networks, or multi-layer networks with shared node sets, present unique challenges for key node identification.
• Existing methods often overlook critical inter-layer information, reducing the reliability of influencer detection.
• Accurate identification of key nodes is crucial for understanding network dynamics and controlling information spread.

Purpose of the Study:

• To propose a novel grey correlation model-based algorithm for robust key node identification in multiplex networks.
• To overcome the limitations of existing methods by integrating diverse network attributes.
• To enhance the accuracy and reliability of influencer detection in complex network structures.

Main Methods:

• Developed a grey correlation model-based algorithm integrating layer importance, intra-layer node importance, and compressed network centrality.
• Employed grey relational analysis to fuse heterogeneous node attributes into a unified significance score.
• Validated the algorithm on synthetic and real-world networks using the SIR epidemic model for performance assessment.

Main Results:

• The proposed algorithm demonstrated superior node ranking accuracy compared to six existing methods.
• Experimental results confirmed the effectiveness of the approach in identifying influential spreaders.
• The method successfully preserved cross-layer topological information, enhancing identification reliability.

Conclusions:

• The novel algorithm effectively identifies key nodes in multiplex networks by systematically combining multiple attributes.
• This approach offers a more reliable and accurate method for influencer detection compared to conventional single-dimensional techniques.
• The findings contribute to a better understanding of information diffusion and network control in complex systems.