Graph Random Neural Network
share
Graph neural networks (GNNs) have generalized deep learning methods into graph-structured data with promising performance on graph mining tasks. However, existing GNNs often meet complex graph structures with scarce labeled nodes and suffer from the limitations of non-robustness, over-smoothing, and overfitting. To address these issues, we propose a simple yet effective GNN framework—Graph Random Neural Network (Grand). Different from the deterministic propagation in existing GNNs, Grand adopts a random propagation strategy to enhance model robustness. This strategy also naturally enables Grand to decouple the propagation from feature transformation, reducing the risks of over-smoothing and overfitting. Moreover, random propagation acts as an efficient method for graph data augmentation. Based on this, we propose the consistency regularization for Grand by leveraging the distributional consistency of unlabeled nodes in multiple augmentations, improving the generalization capacity of the model. Extensive experiments on graph benchmark datasets suggest that Grand significantly outperforms state-of-the-art GNN baselines on semi-supervised graph learning tasks. Finally, we show that Grand mitigates the issues of over-smoothing and overfitting, and its performance is married with robustness.
READ FULL TEXT