注意差距：并行单源最短路径中合成边权与自然边权的脱节
Mind the Gap: The Disconnect Between Synthetic and Natural Edge Weights in Parallel Single-Source Shortest Path
中文总结 AI 辅助
该研究发现并行SSSP算法对边权高度敏感，合成均匀边权的基准评估会改变最优参数配置并反转性能层级，挑战了现有SSSP算法的基准测试标准。
中文摘要 AI 辅助
科学研究常使用合成的均匀分布边权评估并行单源最短路径（SSSP）算法，但现实世界的图呈现出截然不同的、通常为重尾型的权值分布。这在算法评估方式与实际性能间造成了脱节，因为大多数SSSP实现本就依赖权值分布进行参数调优和提升工作效率。本文探究当前基准测试方法是否会无意偏倚这些算法的性能结果。为此，我们统计表征了来自多个领域的17个现实世界图的权值分布，并将其与文献中使用的6种合成分布进行对比。通过对7种最先进的并行SSSP算法的全面评估，我们证明其对边权存在严重敏感性，且使用合成均匀权值进行评估会改变最优参数配置，并可能反转性能层级。这些发现对现有基准测试标准提出了挑战，为严谨的SSSP算法设计提供了实用见解。
英文摘要
Scientific research works often evaluate Parallel Single-Source Shortest Path (SSSP) algorithms using synthetic, uniformly distributed edge weights. However, real-world graphs exhibit very different, often heavy-tailed, weight distributions. This creates a disconnect between how algorithms are evaluated and their real-world performance, since most SSSP implementations inherently rely on the weight distribution for parameter tuning and work efficiency. In this paper, we explore whether current benchmarking methods unintentionally bias the performance results of these algorithms. To this end, we statistically characterize the weight distributions of 17 real-world graphs from a variety of domains and contrast them with six synthetic distributions used in the literature. Through a comprehensive evaluation of seven state-of-the-art parallel SSSP algorithms, we demonstrate severe sensitivity to edge weights, and show that evaluating with synthetic uniform weights alters optimal parameter configurations and can invert the performance hierarchy. These findings challenge existing benchmarking standards and offer practical insights for rigorous SSSP algorithm design.
