  Deep graph neural networks (GNNs) have achieved excellent results on various
tasks on increasingly large graph datasets with millions of nodes and edges.
However, memory complexity has become a major obstacle when training deep GNNs
for practical applications due to the immense number of nodes, edges, and
intermediate activations. To improve the scalability of GNNs, prior works
propose smart graph sampling or partitioning strategies to train GNNs with a
smaller set of nodes or sub-graphs. In this work, we study reversible
connections, group convolutions, weight tying, and equilibrium models to
advance the memory and parameter efficiency of GNNs. We find that reversible
connections in combination with deep network architectures enable the training
of overparameterized GNNs that significantly outperform existing methods on
multiple datasets. 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. To the best of our knowledge, RevGNN-Deep
is the deepest GNN in the literature by one order of magnitude. Please visit
our project website https://www.deepgcns.org/arch/gnn1000 for more information.
