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

This study addresses the limitations of existing key node identification methods for multiplex networks-defined as multi-layer networks where all layers share the same node set. To mitigate the loss of critical inter-layer information and improve the reliability of influencer detection, we propose a grey correlation model-based algorithm. The method integrates three essential attributes of multiplex networks: the importance of each individual layer, the intra-layer importance of each node, and node centrality derived from compressing the multiplex network into a single weighted layer. Grey relational analysis is then employed to fuse these heterogeneous attributes and compute a final node significance score. The proposed algorithm is validated on both synthetic and real-world networks using the SIR epidemic model. Experimental results demonstrate that our approach achieves higher node ranking accuracy than six comparison algorithms, confirming its effectiveness in identifying influential spreaders in multiplex networks. Unlike conventional algorithms that typically rely on single-dimensional information, our method systematically combines multiple attributes of multiplex networks, thereby effectively preserving cross-layer topological information and enhancing the reliability of key node identification.