  Much of the recent progress made in node classification on graphs can be
credited to the careful design on graph neural networks (GNN) and label
propagation algorithms. However, in the literature, in addition to improvements
to the model architecture, there are a number of improvements either briefly
mentioned as implementation details or visible only in source code, and these
overlooked techniques may play a pivotal role in their practical use. In this
paper, we first summarize a collection of existing refinements, and then
propose several novel techniques regarding these model designs and label usage.
We empirically evaluate their impacts on the final model accuracy through
ablation studies, and show that we are able to significantly improve various
GNN models to the extent that they outweigh the gains from model architecture
improvement. Notably, many of the top-ranked models on Open Graph Benchmark
benefit from our techniques.
