Before revision:  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 .
After revision:  Then we leverage consistency regularization to optimize the prediction 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 .