保留因果结构的时序网络高效采样
Efficient Sampling of Temporal Networks with Preserved Causality Structure
AI总结：
本研究将静态网络的经典Color Refinement算法拓展到时序网络，提出可保留$d$跳因果邻域结构的高效采样算法，经理论和实验验证，其保留时序网络关键特征的效果优于现有随机化方法。
摘要：
本文将针对静态网络的经典Color Refinement（颜色精化）算法拓展至无向和有向时序网络，以此设计出一种合成网络采样算法，能够保留给定时序网络的$d$跳邻域结构。$d$的取值越大，原始网络的时序邻域结构保留效果越好。具体而言，我们提出的高效算法可保留网络中长度不超过$d$的时间合规（即“因果”）路径，且可扩展至真实世界的网络规模。我们从理论上（针对度中心性和Katz中心性）和实验上（针对边持续性、因果三角结构和突发性）验证了所提方法的有效性。实验对比表明，与现有随机化方法相比，我们的方法能更有效地保留这些关键时序特征。
英文摘要：
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.
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。
