局部聚合与时间重排下时序网络性质的流：一种用于表征、比较和分类时序网络的工具
Flow of temporal network properties under local aggregation and time shuffling: a tool for characterizing, comparing and classifying temporal networks
AI总结：
提出通过局部时间重排和局部时间聚合变换获取时序网络性质流，并用符号标签总结趋势，从而实现时序网络的比较、模型验证与分类。
摘要：
尽管已经开发并应用了许多工具来表征时序网络，但如何比较它们的问题在很大程度上仍然悬而未决。这确实取决于将哪些特征视为相关的，以及如何量化这些特征中的差异。在本文中，我们提出通过时序网络在时间局部的一般变换下的行为来对其进行表征：(i) 局部时间重排，它破坏小于给定尺度 $b$ 的时间尺度上的相关性，同时保留大的时间尺度；(ii) 在长度为 $n$ 的时间窗口上的局部时间聚合。通过改变 $b$ 和 $n$，我们得到了一个时序网络的流，以及观测值的流，它们在多个时间尺度上编码了时序网络的物理现象。我们使用一种符号方法将这些流总结为描述其趋势的标签（字符串）。然后，这些标签可用于比较时序网络、验证模型或识别具有相似标签的网络组。我们的程序可应用于任何时序网络和任意一组观测值，并且我们在描述各种情境下面对面互动的数据集集合上对其进行了说明，包括实证数据和合成数据。
英文摘要：
Although many tools have been developed and employed to characterize temporal networks, the issue of how to compare them remains largely open. It depends indeed on what features are considered as relevant, and on the way the differences in these features are quantified. In this paper, we propose to characterize temporal networks through their behaviour under general transformations that are local in time: (i) a local time shuffling, which destroys correlations at time scales smaller than a given scale $b$, while preserving large time scales, and (ii) a local temporal aggregation on time windows of length $n$. By varying $b$ and $n$, we obtain a flow of temporal networks, and flows of observable values, which encode the phenomenology of the temporal network on multiple time scales. We use a symbolic approach to summarize these flows into labels (strings of characters) describing their trends. These labels can then be used to compare temporal networks, validate models, or identify groups of networks with similar labels. Our procedure can be applied to any temporal network and with an arbitrary set of observables, and we illustrate it on an ensemble of data sets describing face-to-face interactions in various contexts, including both empirical and synthetic data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
