论文标题
从观察到的骨干中构建替代时间网络数据
Building surrogate temporal network data from observed backbones
论文作者
论文摘要
在许多数据集中，至关重要的元素与非必需的元素和噪声共存。对于特别是网络表示的数据，已经提出了几种方法来提取“网络骨干”，即最重要的链接集。但是，尚未解决如何有效使用数据的压缩视图的问题。在这里，我们通过提出并探索几个系统的过程来解决此问题，以构建各种时间网络骨架的替代数据。特别是，我们探讨了需要与主链一起保留多少有关原始数据的信息，以便可以将替代数据用于扩展过程的数据驱动的数值模拟中。我们使用具有各种结构和属性的经验时间网络来说明我们的结果。
In many data sets, crucial elements co-exist with non-essential ones and noise. For data represented as networks in particular, several methods have been proposed to extract a "network backbone", i.e., the set of most important links. However, the question of how the resulting compressed views of the data can effectively be used has not been tackled. Here we address this issue by putting forward and exploring several systematic procedures to build surrogate data from various kinds of temporal network backbones. In particular, we explore how much information about the original data need to be retained alongside the backbone so that the surrogate data can be used in data-driven numerical simulations of spreading processes. We illustrate our results using empirical temporal networks with a broad variety of structures and properties.
