拓性持久性确定了更高级的网络漏洞
1Department of Data and Systems Engineering, The University of Hong Kong, Pok Fu Lam, Hong Kong, China.
Chaos (Woodbury, N.Y.)
|January 9, 2026
概括
我们开发了一种新方法,通过分析高阶拓特征来识别复杂网络中的关键节点. 这种方法增强了网络分析和稳定性评估,优于传统方法.
科学领域:
- 网络科学 网络科学
- 复杂系统分析 复杂系统分析
- 拓数据分析 拓数据分析
背景情况:
- 传统的图形模型在复杂系统中难以处理多节点交互和多尺度连接.
- 了解具有更高阶依赖性的网络中的结构影响和稳定性是具有挑战性的.
研究的目的:
- 引入一种新型指标,即持久结构影响指标,用于量化复杂网络中节点影响.
- 为了捕捉潜在的更高阶拓特征和局部结构灵敏度.
主要方法:
- 持续同质性与局部几何扰动分析的整合.
- 提取多尺度拓特征和节点影响量化.
- 与基线方法相比,对合成和现实世界的网络进行评估.
主要成果:
- 拟议的模型准确地识别了结构关键节点,导致网络更快地解体.
- 在删除20%的节点后,实现了将巨型组件大小减少到0.12 (与基于度的攻击相比为0.23).
- 与现有措施相比,与地面真相传播动态的相关性提高了高达25.1%.
结论:
- 持久结构影响指标为复杂网络分析提供了强大的和可概括的框架.
- 该方法提供了改进的解释性,计算可处理性和结构忠实性,没有特定领域的先验.
- 这种方法增强了对网络系统中的结构稳定性和影响力的理解.
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