Qiu Fanshuo, Yu Chengpu, Feng Yunji, Li Yao
School of Automation, Beijing Institute of Technology, Beijing, 100081, China.
Sci Rep. 2024 May 27;14(1):12039. doi: 10.1038/s41598-024-62895-2.
Key nodes are similar to important hubs in a network structure, which can directly determine the robustness and stability of the network. By effectively identifying and protecting these critical nodes, the robustness of the network can be improved, making it more resistant to external interference and attacks. There are various topology analysis methods for a given network, but key node identification methods often focus on either local attributes or global attributes. Designing an algorithm that combines both attributes can improve the accuracy of key node identification. In this paper, the constraint coefficient of a weakly connected network is calculated based on the Salton indicator, and a hierarchical tenacity global coefficient is obtained by an improved K-Shell decomposition method. Then, a hierarchical comprehensive key node identification algorithm is proposed which can comprehensively indicate the local and global attributes of the network nodes. Experimental results on real network datasets show that the proposed algorithm outperforms the other classic algorithms in terms of connectivity, average remaining edges, sensitivity and monotonicity.
关键节点类似于网络结构中的重要枢纽，能够直接决定网络的稳健性和稳定性。通过有效地识别和保护这些关键节点，可以提高网络的稳健性，使其更能抵御外部干扰和攻击。对于给定的网络，存在各种拓扑分析方法，但关键节点识别方法通常侧重于局部属性或全局属性。设计一种结合这两种属性的算法可以提高关键节点识别的准确性。本文基于Salton指标计算弱连通网络的约束系数，并通过改进的K-Shell分解方法获得分层韧性全局系数。然后，提出了一种分层综合关键节点识别算法，该算法可以综合表征网络节点的局部和全局属性。在真实网络数据集上的实验结果表明，所提算法在连通性、平均剩余边数、灵敏度和单调性方面优于其他经典算法。
