Review for NeurIPS paper: Towards Deeper Graph Neural Networks with Differentiable Group Normalization

Weaknesses: (1) Empirical results seem to be weak compared to other works [1] aiming at tackling over-smoothing problem. According to table 1, Deep GNNs with DGN outperform those with other normalization mechanisms. However, the performance degradation still exists when the GNNs are made deeper. Though the idea is somewhat incremental, the proposed Differentiable Group Normalization relates it indeed. However, the Instance Information Gain employ mutual information between the input features and output representations as a metric, which seems to be somewhat weird. According to the Appendix F, the output representation is taken from the final prediction layer, which is the result of a linear transformation applied to the top hidden features.