高级网络的简化性 高级网络的简化性
Nicholas W Landry1,2, Jean-Gabriel Young1,2, Nicole Eikmeier3
1Vermont Complex Systems Center, University of Vermont, 82 Innovation PI, 05405 Burlington, USA.
概括
高阶网络揭示了复杂的系统相互作用. 这项研究引入了"简单性"来量化包容性,发现现实世界的网络很少适合完美或缺席的包容性模型,建议新的网络科学方向.
科学领域:
- 网络科学 网络科学
- 复杂系统分析 复杂系统分析
- 数据建模数据建模
背景情况:
- 高阶网络模拟了涉及两个以上实体的交互.
- 现有的模型要么忽略包含,要么假定完美/完整的包含.
- 需要一个细微的方法来评估纳入实证系统.
研究的目的:
- 引入和定义"简单性"作为高阶网络中包容性的衡量标准.
- 分析现实世界复杂系统中简单性的分布.
- 评估当前生成模型捕捉网络包容结构的能力.
主要方法:
- 简化性概念的发展和相关的量化措施.
- 在各种高阶网络数据集中对包含结构的实证分析.
- 对生成模型与观察到的简单性分布进行评估.
主要成果:
- 经验观察到的高阶网络很少表现出完美的或缺席的包容性.
- 现实世界的系统通常显示中级的简化程度.
- 当前的生成模型无法准确地复制这些数据集的包含结构.
结论:
- 简化频谱为更高级的网络分析提供了更现实的框架.
- 现有的建模方法不足以捕捉微妙的纳入模式.
- 这些发现需要为更高阶网络开发新的生成模型.
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