时间网络中的模式与更高层次的自我中心结构
Beatriz Arregui-García1, Antonio Longa2, Quintino Francesco Lotito2
1Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, 07122 Palma de Mallorca, Spain.
Entropy (Basel, Switzerland)
|March 28, 2024
概括
分析复杂的社会动态需要新的方法. 我们引入了超自我中心的时间社区来捕捉群体相互作用,揭示了更高阶结构显著解释了时间网络数据的变化.
科学领域:
- 复杂系统科学 复杂系统科学
- 网络科学 网络科学
- 社交网络分析 社交网络分析
背景情况:
- 时间网络模拟时间演变的相互作用,但往往专注于双对关系.
- 现有的自我中心的时空邻居措施忽视了社会系统中常见的群体互动.
- 高级网络为分析群体相互作用提供了一个框架.
研究的目的:
- 为了将自我中心的时空邻近分析泛化为使用超图的更高阶交互.
- 为分析复杂的社会动态引入"超自我中心的时间社区"的概念.
- 评估高阶相互作用对时间网络分析的影响.
主要方法:
- 将时间网络泛化为超图来表示群体相互作用.
- 定义和应用"超自我中心的时间社区"用于网络分解.
- 分析时间网络数据,包括二次相互作用 (三重组).
主要成果:
- 提出的超自我中心的时间社区有效地捕捉了更高阶的社会互动.
- 第二阶层结构 (三胞胎) 占数据变化的大部分.
- 这种变化在不同的数据集,节点之间以及随着时间的推移而观察到.
结论:
- 高级表示对于全面了解时间社会网络至关重要.
- 超自我中心的时间社区为分析复杂的,基于群体的互动提供了强大的工具.
- 这些发现强调了在复杂系统中超越对对分析的重要性.
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