在时间相互作用数据中推断动态超图表征的推理
1Institute of Data Science, University of Hong Kong, Hong Kong; Department of Urban Planning and Design, University of Hong Kong, Hong Kong; and Urban Systems Institute, University of Hong Kong, Hong Kong.
Physical review. E
|June 22, 2024
概括
这项研究引入了一种新的方法,用于使用超图分析时间事件数据. 它优化了时间窗口的选择,以揭示复杂系统的基础结构,如在线购物和生态相互作用.
科学领域:
- 复杂系统科学 复杂系统科学
- 数据科学数据科学数据科学
- 网络科学 网络科学
背景情况:
- 许多科学领域在不同的项目类别 (例如,用户和产品,昆虫和植物) 之间产生时间标记的交互数据.
- 这些数据集可以被表示为时间超图,但选择最佳时间窗口的快照是具有挑战性的和影响分析.
- 现有的方法缺乏原则性的方法来确定时间快照的数量和持续时间.
研究的目的:
- 开发一种以原则为导向的数据驱动方法,从事件数据中提取最佳的时间超图快照.
- 为了应对选择适当的时间窗口的挑战,以超图形表示时间相互作用.
- 加强对时间变化的交互数据集中的结构规律的分析.
主要方法:
- 提出了一个基于最小描述长度 (MDL) 原则的非参数解决方案.
- 开发了一种方法来提取时间超图快照,以最佳方式捕捉结构规律.
- 在合成和现实数据集上应用和验证了该方法,包括人类移动性数据.
主要成果:
- 拟议的方法成功地从噪音数据中恢复了植入的超图结构.
- 证明了揭示人类移动模式的有意义波动的能力.
- 基于MDL的方法为时间超图构建提供了最佳的数据驱动策略.
结论:
- 开发的方法为模拟时间事件数据作为超图提供了强大的解决方案.
- 优化时间快照提取可以提高网络结构和动态的发现.
- 这种方法在社会科学和自然科学中广泛适用,用于分析复杂的相互作用.
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