Temporal networks are essential for modeling and understanding systems whose behavior varies in time, from social interactions to biological systems. Often, however, real-world data are prohibitively expensive to collect or unshareable due to privacy concerns. A promising solution is `surrogate networks', synthetic graphs with the properties of real-world networks. Until now, the generation of realistic surrogate temporal networks has remained an open problem, due to the difficulty of capturing both the temporal and topological properties of the input network, as well as their correlations, in a scalable model. Here, we propose a novel and simple method for generating surrogate temporal networks. By decomposing graphs into temporal neighborhoods surrounding each node, we can generate new networks using neighborhoods as building blocks. Our model vastly outperforms current methods across multiple examples of temporal networks in terms of both topological and dynamical similarity. We further show that beyond generating realistic interaction patterns, our method is able to capture intrinsic temporal periodicity of temporal networks, all with an execution time lower than competing methods by multiple orders of magnitude.
翻译:时间网络对于从社会互动到生物系统等不同时间的行为变化的模型和理解系统至关重要。然而,现实世界数据往往过于昂贵,难以收集或无法分配,因为隐私问题。一个很有希望的解决办法是“覆盖网络 ”, 具有现实世界网络特性的合成图。到目前为止,现实的替代时间网络的生成仍是一个尚未解决的问题,因为很难在可变模型中捕捉输入网络的时间和地形特性及其相互关系。在这里,我们提出了一个创造替代时间网络的新颖而简单的方法。通过将图表分解到每个节点周围的时区,我们可以产生新的网络,将社区作为建筑块。我们的模型在地形和动态相似性两方面都大大超越了当前时间网络的多种实例。我们进一步表明,除了产生现实的互动模式之外,我们的方法能够捕捉时间网络的内在时间周期,所有时间的运行时间都比相互竞争的方法要小得多。