Abstract: Critical node identification has been a research focus in social system, biological system, power system, and transportation system. Existing works exhibit excessive reliance on node degree, k-shell values, or their simplistic combinations while neglecting the influence of adjacent nodes and global positional information. This article proposes a multi-feature gravity model algorithm, termed as HKGM, to identify key nodes within complex networks. Specifically, the proposed scheme comprehensively considers node degree, local propagation capacity involving both first-order and second-order neighboring nodes, and introduces the global location information of nodes, aiming to construct an evaluation scheme that takes into account both the local and global properties of the network. Meanwhile, in response to the issues of algorithm complexity and computational cost in large-scale networks, this study optimizes the computational efficiency of the proposed scheme. To validate its effectiveness, simulation experiments are conducted on nine real-world datasets, comparing HKGM against nine classical algorithms. Results demonstrate that the proposed method outperforms others under evaluation metrics including the SIR propagation model, Kendall correlation coefficient, and CCDF monotonicity function. These findings confirm that HKGM achieves superior discrimination accuracy in key node identification tasks for complex networks, significantly enhancing detection accuracy.

Key words: gravity model, H-index, node influence, crucial node identification, complex networks