  Consistency training is a popular method to improve deep learning models in
computer vision and natural language processing. Graph neural networks (GNNs)
have achieved remarkable performance in a variety of network science learning
tasks, but to date no work has studied the effect of consistency training on
large-scale graph problems. GNNs scale to large graphs by minibatch training
and subsample node neighbors to deal with high degree nodes. We utilize the
randomness inherent in the subsampling of neighbors and introduce a novel
consistency training method to improve accuracy. For a target node we generate
different neighborhood expansions, and distill the knowledge of the average of
the predictions to the GNN. Our method approximates the expected prediction of
the possible neighborhood samples and practically only requires a few samples.
We demonstrate that our training method outperforms standard GNN training in
several different settings, and yields the largest gains when label rates are
low.
