新兴活动时间超图:用于生成现实的时间变化的超图的模型.
Marco Mancastroppa1, Giulia Cencetti1, Alain Barrat1
1CPT, CNRS, Université de Toulon, Aix Marseille Univ, Turing Center for Living Systems, 13009 Marseille, France.
Physical review. E
|December 23, 2025
概括
一个新的模型,即新兴活动时间超图 (EATH),生成合成时间超图,模仿现实世界的群体互动. 这允许更好地理解复杂的系统和动态过程,即使数据有限.
科学领域:
- 复杂系统科学 复杂系统科学
- 网络科学 网络科学
- 数据科学数据科学数据科学
背景情况:
- 时间变化的群体相互作用是复杂系统的基础.
- 时间超图捕捉了更高阶的,时间依赖的相互作用.
- 经验数据集往往不完整,需要替代模型.
研究的目的:
- 引入一种新的时间超图模型 (EATH),用于生成合成数据集.
- 能够研究复杂交互网络上的动态过程.
- 促进对具有有限或不完整交互数据的系统的理解.
主要方法:
- 开发了新兴活动时间超图 (EATH) 模型.
- EATH使用节点活动动态和内存机制来产生交互.
- 与经验面对面互动数据集相对应的验证EATH.
主要成果:
- EATH成功生成了替代时空超图,反映了经验数据属性.
- 高级传染动态的模拟显示了真实和合成数据的可比结果.
- 证明了EATH在创建可调和混合超图的灵活性.
结论:
- EATH模型提供了一个强大的工具,用于创建现实的合成时间超图.
- 合成数据有助于研究数据收集具有挑战性的复杂系统动态.
- 开辟了理解群体互动中出现的行为新途径.
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