从时间序列数据中对称性先前基于更高阶网络的重建
Man Yan1, Hai-Feng Zhang1, Huan Wang2
1School of Mathematical Science, Anhui University, Hefei 230601, China.
Chaos (Woodbury, N.Y.)
|March 2, 2026
概括
从时间序列数据中重建复杂的高级网络具有挑战性. 本研究引入了一种使用对称性先验的新方法,以准确有效地揭示网络结构,改进现有技术.
科学领域:
- 网络科学 网络科学
- 数据科学数据科学数据科学
- 计算数学 计算数学 计算数学
背景情况:
- 高阶网络模拟了超越对对关系的复杂相互作用.
- 现实世界的网络拓通常是不可观察的,只有节点动态时间序列数据可用.
- 由于现有方法的局限性,精确重建复杂的高级网络仍然是一个重大挑战.
研究的目的:
- 从时间序列数据中重建更高阶网络的准确和有效方法.
- 通过结合固有的对称性先验来解决当前方法的局限性.
- 提高高级网络重建的实际应用性和可扩展性.
主要方法:
- 将对称性先验纳入重建过程中.
- 在代方程中嵌入对称约束.
- 使用区块坐标下降方法来解决.
主要成果:
- 实现了与传统全球方法相美的重建准确度.
- 证明了接近点对点方法的计算效率.
- 通过理论分析和数值实验验验证了拟议的方法.
结论:
- 拟议的方法为更高阶网络重建提供了一个实用且可扩展的解决方案.
- 整合对称性先验可以提高准确性并降低计算复杂性.
- 这种方法为分析不直接观察网络结构的复杂系统提供了有价值的工具.
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