广西师范大学学报（自然科学版）2026，Vol.44Issue(2)：132-144,13.DOI:10.16088/j.issn.1001-6600.2025041001
复杂网络中基于多特征引力模型的关键节点识别方法
Critical Node Identification in Complex Network Based on Multi-feature Gravity Model
摘要
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.关键词
引力模型/H指数/节点影响力/关键节点识别/复杂网络Key words
gravity model/H-index/node influence/crucial node identification/complex networks分类
信息技术与安全科学引用本文复制引用
陈斯淋,刘佳飞,周何馨,吴璟莉,李高仕..复杂网络中基于多特征引力模型的关键节点识别方法[J].广西师范大学学报（自然科学版）,2026,44(2):132-144,13.基金项目
国家自然科学基金(62302107,62366007) （62302107,62366007）
广西自然科学基金(2025GXNSFBA069563,2025GXNSFAA069507) （2025GXNSFBA069563,2025GXNSFAA069507）
广西多源信息挖掘与安全重点实验室系统性研究课题基金(24-A-03-01,24-A-03-02) （24-A-03-01,24-A-03-02）
