Zhang Jieyong, Liang Wei, Sun Peng, Zhao Liang
Information and Navigation College, Air Force Engineering University, Xi'an, 710077, China.
Sci Rep. 2025 Jul 6;15(1):24119. doi: 10.1038/s41598-025-07935-1.
In the context of networked systems, identifying key objectives is crucial for optimizing system efficiency and enhancing capabilities. This study addresses the propagation characteristics of system networks with fixed structures and proposes an improved K-shell decomposition method based on second-order degree decomposition. The traditional degree-based decomposition step is replaced with a second-order degree-based decomposition step. Additionally, within the same second-order degree decomposition KS layer, an enhanced network constraint coefficient is introduced to determine whether nodes within the same KS layer have more structural hole connections. This approach aims to provide a more comprehensive and accurate reflection of the importance of nodes within the network.The proposed method effectively evaluates key propagation nodes in different networks through improved K-shell decomposition and network constraint coefficients. The paper systematically proposes an improved K-shell method based on second-order degree decomposition combined with a network constraint coefficient, validating its efficiency and accuracy in identifying critical propagation nodes through contextual analysis, methodological innovation, experiments on real and synthetic datasets, and comparisons of Kendall's coefficient and SIR propagation simulations.
在网络系统的背景下，识别关键目标对于优化系统效率和增强能力至关重要。本研究探讨了具有固定结构的系统网络的传播特性，并提出了一种基于二阶度分解的改进K壳分解方法。传统的基于度的分解步骤被基于二阶度的分解步骤所取代。此外，在同一二阶度分解的KS层内，引入了增强的网络约束系数，以确定同一KS层内的节点是否具有更多的结构洞连接。该方法旨在更全面、准确地反映网络中节点的重要性。所提出的方法通过改进的K壳分解和网络约束系数有效地评估了不同网络中的关键传播节点。本文系统地提出了一种基于二阶度分解并结合网络约束系数的改进K壳方法，通过情境分析、方法创新、在真实和合成数据集上的实验以及肯德尔系数比较和SIR传播模拟，验证了其在识别关键传播节点方面的效率和准确性。
