Important node identification for complex networks based on improved Electre multi-attribute fusion

Identifying important nodes is a long-standing topic of discussion in the field of complex networks. Most current methods focus on either local or global attributes of nodes, or a simple combination of both. However, in real-world networks, the influence of local and global attributes on nodes varies significantly. Therefore, determining how to accurately assess the weights of different attributes for different networks to enhance the identification of important nodes remains an urgent challenge. In this paper, we propose a multi-attribute decision fusion method named SK-E. This method constructs both local and global metrics for diverse networks and employs an improved Electre approach to fuse these metrics. By determining the optimal weight between local and global metrics, SK-E effectively identifies important nodes in network datasets with varying topological structures. Evaluated using four indices (SIR epidemic model, independent cascade model, Kendall coefficient and constraint efficiency) across nine real networks, the proposed method exhibits superior accuracy compared to existing methods.