在高阶网络中随机步行的动态波动
Leonardo Di Gaetano1, Giorgio Carugno2, Federico Battiston1
1Department of Network and Data Science, <a href="https://ror.org/02zx40v98">Central European University</a>, 1100 Vienna, Austria.
Physical review letters
|September 20, 2024
概括
高阶相互作用影响网络中的动态过程和罕见事件. 这项研究揭示了这些相互作用如何抑制或促进波动,为网络动态提供了洞察力.
科学领域:
- 复杂的系统复杂的系统.
- 网络科学 网络科学
- 统计物理 统计物理
背景情况:
- 高阶相互作用在动态过程中塑造集体行为.
- 这些相互作用在驱动罕见事件和波动中的作用在很大程度上仍未被探索.
- 了解波动对于预测系统行为至关重要.
研究的目的:
- 研究在高阶网络上的随机走路中出现的波动和罕见事件的出现.
- 分析网络结构,特别是高阶交互如何影响动态波动.
- 开发一个理论框架,用于更高阶系统的波动.
主要方法:
- 使用大偏差理论来分析灭的 (固定的) 超图结构.
- 采用位点近似用于回暖 (时间演变) 场景.
- 在高阶网络上建模随机步行.
主要成果:
- 在灭的网络中,具有更高阶交互的节点抑制罕见事件,而其他节点则促进它们.
- 在回火网络中,动态波动被放大.
- 最佳的高阶配置可以预测极端波动.
结论:
- 高阶相互作用在调节罕见事件和波动方面发挥着关键作用.
- 这项研究为理解高阶网络的动态提供了理论基础.
- 这些发现对依赖网络分析和动态系统的领域有影响.
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