高级网络的可缩小性从动态来看
Maxime Lucas1,2,3, Luca Gallo4,5,6, Arsham Ghavasieh7
1Department of Mathematics and Namur Institute for Complex Systems (naXys), Université de Namur, Namur, Belgium. maxime.lucas@unamur.be.
Nature communications
|January 15, 2026
概括
我们开发了一种信息理论方法来评估复杂系统是否从更高阶的网络模型中获益,而不是更简单的双向网络模型. 一些系统保留了高阶结构,而另一些系统则简化为对互动.
科学领域:
- 复杂系统科学 复杂系统科学
- 网络科学 网络科学
- 信息理论 信息理论
背景情况:
- 复杂系统表现出双向和高阶相互作用,这对集体现象至关重要.
- 高级网络模型提供了优越的描述,但增加了复杂性和计算成本.
- 需要一种定量方法来证明高阶建模与双向方法相比.
研究的目的:
- 开发一个定量框架来评估复杂系统中更高层次相互作用的必要性.
- 为了确定何时高阶网络模型相比于对式模型是有利的.
- 在将高阶结构减少为低阶结构时保存的信息进行量化.
主要方法:
- 建议一个信息理论框架来量化高阶相互作用的性成本和可区分性.
- 该框架评估了网络结构如何影响扩散行为.
- 使用受控随机化程序来调查可减少性,重点关注嵌套性和异质度.
主要成果:
- 经验分析表明,一些系统保留了必要的更高阶结构.
- 其他技术和生物网络显示了更高层次的结构崩到双对相互作用.
- 嵌套性和异质度在更高阶结构的可还原性中起作用.
结论:
- 拟议的框架提供了一种方法来评估复杂系统网络结构的可还原性.
- 它有助于最大限度地减少模型的维度,同时保留基本的功能信息.
- 这些发现指导了为各种经验系统选择合适的网络模型.
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