Identifying influential nodes through hierarchical k-shell and extended neighborhood integration

The identification of influential nodes has extensive applications in complex network research. To address the challenge of balancing accuracy and computational efficiency in existing methods, this paper proposes an algorithm named HKEN that integrates hierarchical k-shell decomposition with extended neighborhood information. The approach incorporates both global structural features and local topological information of nodes. First, the coarse-grained hierarchical mechanism of the k-shell algorithm is optimized, and the initial weight of nodes is dynamically computed based on their degree and k-shell values. Second, the neighborhood range of nodes is extended, and a local clustering coefficient is introduced to establish a threshold for information transmission distance. Finally, an influence aggregation strategy based on the Jaccard similarity coefficient is proposed. The performance of HKEN was validated through comparative experiments on 10 real-world networks against 12 benchmark methods. Experimental results demonstrate that the proposed method achieves higher consistency with SIR model outcomes and enhances the propagation capability of top-ranked nodes, confirming its improved identification accuracy.