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The recent Long-Range Graph Benchmark (LRGB, Dwivedi et al. 2022) introduceda set of graph learning tasks strongly dependent on long-range interactionbetween vertices. Empirical evidence suggests that on these tasks GraphTransformers significantly outperform Message Passing GNNs (MPGNNs). In thispaper, we carefully reevaluate multiple MPGNN baselines as well as the GraphTransformer GPS (Ramp\'a\v{s}ek et al. 2022) on LRGB. Through a rigorousempirical analysis, we demonstrate that the reported performance gap isoverestimated due to suboptimal hyperparameter choices. It is noteworthy thatacross multiple datasets the performance gap completely vanishes after basichyperparameter optimization. In addition, we discuss the impact of lackingfeature normalization for LRGB's vision datasets and highlight a spuriousimplementation of LRGB's link prediction metric. The principal aim of our paperis to establish a higher standard of empirical rigor within the graph machinelearning community.

Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark