标题： 哪里去了？ 重新评估长距离图基准 显示英文标题

标题： Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark

摘要： 最近的长距离图基准测试（LRGB，Dwivedi 等，2022）引入了一组严重依赖于顶点之间长距离交互的图学习任务。实证证据表明，在这些任务上，图变换器显著优于消息传递图神经网络（MPGNN）。在本文中，我们在 LRGB 上仔细重新评估了多个 MPGNN 基线以及图变换器 GPS（Rampášek 等，2022）。通过严格的实证分析，我们证明由于次优超参数选择，报告的性能差距被高估了。值得注意的是，在多个数据集上，经过基本的超参数优化后，性能差距完全消失。此外，我们讨论了 LRGB 视觉数据集缺乏特征归一化的影响，并强调了 LRGB 链接预测指标的一个虚假实现。我们论文的主要目的是在图机器学习社区内建立更高的实证严谨性标准。 显示英文摘要

摘要： The recent Long-Range Graph Benchmark (LRGB, Dwivedi et al. 2022) introduced a set of graph learning tasks strongly dependent on long-range interaction between vertices. Empirical evidence suggests that on these tasks Graph Transformers significantly outperform Message Passing GNNs (MPGNNs). In this paper, we carefully reevaluate multiple MPGNN baselines as well as the Graph Transformer GPS (Ramp\'a\v{s}ek et al. 2022) on LRGB. Through a rigorous empirical analysis, we demonstrate that the reported performance gap is overestimated due to suboptimal hyperparameter choices. It is noteworthy that across multiple datasets the performance gap completely vanishes after basic hyperparameter optimization. In addition, we discuss the impact of lacking feature normalization for LRGB's vision datasets and highlight a spurious implementation of LRGB's link prediction metric. The principal aim of our paper is to establish a higher standard of empirical rigor within the graph machine learning community.