Review for NeurIPS paper: Graph Random Neural Networks for Semi-Supervised Learning on Graphs

Weaknesses: The proposed methods are not that novel. More specifically: (1) It seems that the consistency regularization is a general framework that can combine with other data augmentation methods, such as dropedge, and sampling algorithms. It would be better if the authors can also try these combinations, instead of only adopting their proposed dropnode augmentation. Thus, it would be better if the authors can provide a curve showing the performance of the proposed framework against other baselines under different training data percentage. Also, better to combine these methods with some advanced base GNN.