Before revision:  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 architectureimprovement . Notably, many of the top-ranked models on  Open Graph Benchmark  benefit from our techniques.
After revision:  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 model architectureimprovement . Notably, many of the top-ranked models on Open Graph Benchmark benefit from our techniques.