使用顺序过渡方法揭示复杂网络的连接性
Juan A Almendral1,2, I Leyva1,2, Irene Sendiña-Nadal1,2
1Complex Systems Group & Grupo Interdisciplinar de Sistemas Complejos (GISC), Universidad Rey Juan Carlos, 28933 Móstoles, Madrid, Spain.
Entropy (Basel, Switzerland)
|July 29, 2023
概括
我们介绍了一种用于分析复杂网络的新型变量测量方法. 这种方法有效地区分了网络混乱系统的拓作用,超过了现有的复杂度指标.
科学领域:
- 复杂系统科学 复杂系统科学
- 网络科学 网络科学
- 非线性动力学是一种非线性动力学.
背景情况:
- 顺序测量对于相关数据序列分析是有用的.
- 在使用顺序方法的动态系统中,了解信息流和网络结构尚未得到充分的探索.
- 现有的方法在同步过程中难以区分网络角色.
研究的目的:
- 为了比较序列变量与基于序列过渡矩阵的新序列变量测量.
- 评估这些措施在识别网络混乱系统中节点的拓角色方面的有效性.
- 为了证明新方法对现实世界时间序列数据的适用性.
主要方法:
- 计算时间序列数据的顺序变量.
- 开发和应用基于顺序过渡概率矩阵的变量.
- 在网络混沌的罗斯勒系统上测试了测试措施.
- 使用来自非线性振荡器网络的实验数据验证了该方法.
主要成果:
- 顺序过渡矩阵的顺序 Entropy显著超过了标准的顺序 Entropy.
- 提出的方法有效地区分了网络混乱系统中节点的拓角色.
- 该方法在分析实验时间序列数据方面表现出强度.
结论:
- 顺序过渡矩阵的变为网络分析提供了一个强大的新工具.
- 这种方法可以从时间序列数据中发现网络结构,即使是未知的底层网络.
- 该方法适用于各种现实世界系统,展示相关性和网络结构.
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