标题： 具有保留因果结构的时间网络高效采样 显示英文标题

标题： Efficient Sampling of Temporal Networks with Preserved Causality Structure

摘要： 在本文中，我们将经典的色彩细化算法从静态网络扩展到时序（无向和有向）网络。 这使我们能够设计一种算法来生成保持给定时序网络的$d$-跳邻域结构的合成网络。 $d$越高，原始网络的时间邻域结构保留得越好。 具体来说，我们提供了高效的算法，在网络中保留至长度$d$的时间尊重（“因果”）路径，并能扩展到现实世界的网络规模。 我们通过理论（度数和Katz中心性）和实验（边持续性、因果三角形和突发性）验证了我们的方法。 实验比较显示，与现有的随机化方法相比，我们的方法更有效地保留这些关键的时间特征。 显示英文摘要

摘要： In this paper, we extend the classical Color Refinement algorithm for static networks to temporal (undirected and directed) networks. This enables us to design an algorithm to sample synthetic networks that preserves the $d$-hop neighborhood structure of a given temporal network. The higher $d$ is chosen, the better the temporal neighborhood structure of the original network is preserved. Specifically, we provide efficient algorithms that preserve time-respecting ("causal") paths in the networks up to length $d$, and scale to real-world network sizes. We validate our approach theoretically (for Degree and Katz centrality) and experimentally (for edge persistence, causal triangles, and burstiness). An experimental comparison shows that our method retains these key temporal characteristics more effectively than existing randomization methods.