  Graph Transformers (GTs) have recently emerged as popular alternatives to
traditional message-passing Graph Neural Networks (GNNs), due to their
theoretically superior expressiveness and impressive performance reported on
standard node classification benchmarks, often significantly outperforming
GNNs. In this paper, we conduct a thorough empirical analysis to reevaluate the
performance of three classic GNN models (GCN, GAT, and GraphSAGE) against GTs.
Our findings suggest that the previously reported superiority of GTs may have
been overstated due to suboptimal hyperparameter configurations in GNNs.
Remarkably, with slight hyperparameter tuning, these classic GNN models achieve
state-of-the-art performance, matching or even exceeding that of recent GTs
across 17 out of the 18 diverse datasets examined. Additionally, we conduct
detailed ablation studies to investigate the influence of various GNN
configurations, such as normalization, dropout, residual connections, network
depth, and jumping knowledge mode, on node classification performance. Our
study aims to promote a higher standard of empirical rigor in the field of
graph machine learning, encouraging more accurate comparisons and evaluations
of model capabilities.
