  We study the problem of semi-supervised learning on graphs, for which graph
neural networks (GNNs) have been extensively explored. However, most existing
GNNs inherently suffer from the limitations of over-smoothing, non-robustness,
and weak-generalization when labeled nodes are scarce. In this paper, we
propose a simple yet effective framework -- GRAPH RANDOM NEURAL NETWORKS
(GRAND) -- to address these issues. In GRAND, we first design a random
propagation strategy to perform graph data augmentation. Then we leverage
consistency regularization to optimize the prediction consistency of unlabeled
nodes across different data augmentations. Extensive experiments on graph
benchmark datasets suggest that GRAND significantly outperforms
state-of-the-art GNN baselines on semi-supervised node classification. Finally,
we show that GRAND mitigates the issues of over-smoothing and non-robustness,
exhibiting better generalization behavior than existing GNNs. The source code
of GRAND is publicly available at https://github.com/Grand20/grand.
