  We present the SCR framework for enhancing the training of graph neural
networks (GNNs) with consistency regularization. Regularization is a set of
strategies used in Machine Learning to reduce overfitting and improve the
generalization ability. However, it is unclear how to best design the
generalization strategies in GNNs, as it works in a semi-supervised setting for
graph data. The major challenge lies in how to efficiently balance the
trade-off between the error from the labeled data and that from the unlabeled
data. SCR is a simple yet general framework in which we introduce two
strategies of consistency regularization to address the challenge above. One is
to minimize the disagreements among the perturbed predictions by different
versions of a GNN model. The other is to leverage the Mean Teacher paradigm to
estimate a consistency loss between teacher and student models instead of the
disagreement of the predictions. We conducted experiments on three large-scale
node classification datasets in the Open Graph Benchmark (OGB). Experimental
results demonstrate that the proposed SCR framework is a general one that can
enhance various GNNs to achieve better performance. Finally, SCR has been the
top-1 entry on all three OGB leaderboards as of this submission.
