Over the past few years, graph neural networks (GNN) and label
propagation-based methods have made significant progress in addressing node
classification tasks on graphs. However, in addition to their reliance on
elaborate architectures and algorithms, there are several key technical details
that are frequently overlooked, and yet nonetheless can play a vital role in
achieving satisfactory performance. In this paper, we first summarize a series
of existing tricks-of-the-trade, and then propose several new ones related to
label usage, loss function formulation, and model design that can significantly
improve various GNN architectures. We empirically evaluate their impact on
final node classification accuracy by conducting ablation studies and
demonstrate consistently-improved performance, often to an extent that
outweighs the gains from more dramatic changes in the underlying GNN
architecture. Notably, many of the top-ranked models on the Open Graph
Benchmark (OGB) leaderboard and KDDCUP 2021 Large-Scale Challenge MAG240M-LSC
benefit from these techniques we initiated.

本文概述了一系列现有的技巧，并提出了一些与标签使用、损失函数制定和模型设计相关的新技巧，以显著提高各种图神经网络 (GNN) 架构的性能。通过进行消融研究，我们经验性地评估了它们对最终节点分类精度的影响，并证明了它们的性能得到了不断改进，通常超过了底层 GNN 架构变化所带来的收益。值得注意的是，许多排名前列的模型从中受益，这是我们提出的一些技术。