基于KPDN-INCC方法的复杂网络中重要的节点的动态识别
Jieyong Zhang1, Liang Zhao2, Peng Sun3
1Information and Navigation College, Air Force Engineering University, Xi'an, 710077, China.
Scientific reports
|March 9, 2024
概括
在动态网络中识别关键节点是一项挑战. 本研究提出了一种新方法,将本地和全球网络属性整合起来,以准确地确定重要节点,帮助网络解体,并改进现有技术.
科学领域:
- 复杂网络分析 复杂网络分析
- 网络科学 网络科学
- 系统工程是系统工程.
背景情况:
- 级联故障在复杂网络中构成重大风险.
- 现有的节点重要性评估方法通常不适合动态网络场景.
- 区分静态和动态网络节点识别是至关重要的.
研究的目的:
- 为动态复杂网络开发一个强大的节点重要性评估方法.
- 在应用于动态系统时解决静态网络方法的局限性.
- 为了提高识别网络解体关键节点的准确性.
主要方法:
- 整合本地和全球相关性属性用于节点评估.
- 改进的k-shell方法与融合度,用于增强全球节点排名.
- 纳入改进的Solton和结构洞因子 (通过INCC) 来识别本地节点关系.
主要成果:
- 拟议的KPDN-INCC方法准确地识别动态网络中的重要节点.
- 这种方法补充了现有的KPDN技术,提供了更好的性能.
- 在具有低随机性的小世界网络中证明了有效性.
结论:
- KPDN-INCC方法为动态复杂网络中节点重要性评估提供了一种优越的方法.
- 精确识别关键节点有助于有效的网络解体.
- 这些发现有助于更好地了解网络弹性和脆弱性.
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