Review for NeurIPS paper: Scalable Graph Neural Networks via Bidirectional Propagation

Additional Feedback: Major comments: * Authors should discuss the limitations of their approach, e.g.: * Can one say how much performance is lost (if any) due to decoupling predictions from propgations, e.g. Is GBP potentially suited for incorporating those? For example, SGC can achieve competitive performance on PPI when increasing the feature dimensionality to 1024 or 2048. Did the authors tune the reported baselines? Minor comments/questions: * Line 40: "In theory, this complexity is undesirable for scalable GNNs": The authors should discuss this in more depth.