Before revision:  Our models RevGNN-Deep (1001 layers with 80 channels each) and RevGNN-Wide (448 layers with 224 channels each) were both trained on a single commodity GPU and achieve an ROC-AUC of 87.74 \pm 0.13 and 88.14 \pm 0.15 on the ogbn-proteins dataset.
After revision:  Our models RevGNN-Deep (1001 layers with 80 channels each) and RevGNN-Wide (448 layers with 224 channels each) were both trained on a single commodity GPU and achieve an ROC-AUC of 87.74 \pm 0.13 and 88.24 \pm 0.15 on the ogbn-proteins dataset.