悬挂的中心性通过通过链接删除来评估网络稳定性来突出关键节点
Ubaida Fatima1, Saman Hina2, Muhammad Wasif3
1Department of Mathematics, NED University of Engineering and Technology, Karachi, Pakistan. ubaida@neduet.edu.pk.
Scientific reports
|November 20, 2025
概括
这项研究介绍了Dangling Centrality,一种新的网络分析指标. 它通过测量它们的移除如何影响系统动态来识别关键节点,帮助网络弹性和设计.
科学领域:
- 网络科学 网络科学
- 图形理论 图形理论
- 系统生物学 系统生物学
背景情况:
- 识别关键节点对于理解网络结构和功能至关重要.
- 传统的中心性指标可能无法完全捕捉节点移除对系统动态的影响.
- 现实世界的网络表现出复杂的相互依赖性,需要先进的分析工具.
研究的目的:
- 引入和验证"Dangling Centrality",一种用于识别网络中关键节点的新型指标.
- 评估节点链接删除对整体系统动态的影响.
- 为各种真实世界的系统提供适用于网络分析的新视角.
主要方法:
- 基于链接删除影响的"Dangling Centrality"指标的开发.
- 在各种现实数据集上验证指标:亚马逊产品网络,蛋白质-蛋白质相互作用 (PPI) 网络和比特币网络.
- 使用已建立的中心度指标 (皮尔森,斯皮尔曼,肯德尔系数) 进行相关性分析.
主要成果:
- "Dangling Centrality"有效地识别了不同网络类型的关键节点,包括关键产品,蛋白质和有影响力的实体.
- 该指标与传统的中心性指标保持一致,同时提供独特的见解.
- 对小规模网络的分析证实了在更简单的环境中指标的行为.
结论:
- "Dangling Centrality"是了解网络漏洞和增强系统弹性的一种有价值的工具.
- 该指标为网络设计和维护提供了实用的见解.
- 该研究提供了一种新的网络分析方法,具有广泛的适用性.
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