A Multi-Attribute Model to Identify Influential Nodes in Complex Networks: Addressing the K-Shell Limitations

Determining the influence capability of different nodes in complex networks has become increasingly important. Its applications include preventing disease spread, controlling rumor propagation, and maximizing product sales. Various methods have been designed for this purpose, using the structural properties of the graph. However, these methods still lack high resolution and accuracy. This paper addresses the problems of core-like groups and low resolution of the k-shell. We combine the k-shell index and degree, and finally present a formula that determines the influence capability of each node. Evaluations are conducted using the Susceptible-Infected-Recovered (SIR) model on 7 real-world networks, comparing 10 methods. The results show that the proposed method has linear time complexity, achieves higher accuracy and resolution than benchmark methods.