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We study the problem of semi-supervised learning on graphs, for which graphneural networks (GNNs) have been extensively explored. However, most existingGNNs inherently suffer from the limitations of over-smoothing, non-robustness,and weak-generalization when labeled nodes are scarce. In this paper, wepropose a simple yet effective framework -- GRAPH RANDOM NEURAL NETWORKS(GRAND) -- to address these issues. In GRAND, we first design a randompropagation strategy to perform graph data augmentation. Then we leverageconsistency regularization to optimize the prediction consistency of unlabelednodes across different data augmentations. Extensive experiments on graphbenchmark datasets suggest that GRAND significantly outperformsstate-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 codeof GRAND is publicly available at https://github.com/Grand20/grand.

Graph Random Neural Network for Semi-Supervised Learning on Graphs