Mind the Gap: The Disconnect Between Synthetic and Natural Edge Weights in Parallel Single-Source Shortest Path

2026-07-29

AI Summary

This study addresses a critical gap in the evaluation of parallel single-source shortest path (SSSP) algorithms, which commonly rely on synthetic edge weights drawn from uniform distributions—despite real-world graphs typically exhibiting heavy-tailed weight distributions. Through a systematic analysis of edge-weight distributions across 17 real-world graphs and comparison with six synthetic distributions, the work evaluates the sensitivity of seven state-of-the-art parallel SSSP algorithms to weight distribution characteristics. It reveals, for the first time, a significant distributional disconnect between synthetic and real edge weights, demonstrating that current benchmarking practices can mislead performance assessments: uniform weights not only substantially alter optimal algorithmic parameter configurations but can even invert relative algorithm rankings. These findings challenge prevailing SSSP evaluation paradigms and advocate for more realistic guidelines in both algorithm design and empirical evaluation.

Abstract

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.

Problem

• Parallel Single-Source Shortest Path
• edge weight distribution
• synthetic benchmarks
• real-world graphs
• algorithm evaluation

Innovation

• edge weight distribution
• parallel SSSP
• real-world graphs
• benchmarking bias
• heavy-tailed distribution