Before revision:  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.
After revision:  Different from the deterministic propagation in existing GNNs, Grand adopts a random propagation strategy to perform graph data augmentation.