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.

该论文通过严格的经验分析重新评估了多个 MPGNN 和 Graph Transformer 的基准性能，证明了由于次优的超参数选择而导致性能差距被高估，并突出了特征归一化和复原的联系预测指标的影响，旨在建立图机器学习社区内更高的经验严谨标准。

差距去哪了？重新评估远程图基准

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

In this paper, we derive generalization bounds for the two primary classes of
graph neural networks (GNNs), namely graph convolutional networks (GCNs) and
message passing GNNs (MPGNNs), via a PAC-Bayesian approach. Our result reveals
that the maximum node degree and spectral norm of the weights govern the
generalization bounds of both models. We also show that our bound for GCNs is a
natural generalization of the results developed in arXiv:1707.09564v2 [cs.LG]
for fully-connected and convolutional neural networks. For message passing
GNNs, our PAC-Bayes bound improves over the Rademacher complexity based bound
in arXiv:2002.06157v1 [cs.LG], showing a tighter dependency on the maximum node
degree and the maximum hidden dimension. The key ingredients of our proofs are
a perturbation analysis of GNNs and the generalization of PAC-Bayes analysis to
non-homogeneous GNNs. We perform an empirical study on several real-world graph
datasets and verify that our PAC-Bayes bound is tighter than others.

本文基于 PAC-Bayesian 方法推导出了两种主要的图神经网络（GCNs 和 MPGNNs）的泛化界，进一步显示节点最大度数和权重的谱范数支配了这两种模型的泛化界。