  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.
