1. Lack of the explanation about the importance and the necessity to design deep GNN models . In this paper, the author tries to address the issue of over-smoothing and build deeper GNN models. However, there is no explanation about why should we build a deep GNN model. For CNN, it could be built for thousands of layers with significant improvement of the performance. While for GNN, the performance decreases with the increase of the depth (shown in Figure 1). Since the deeper GNN model does not show the significant improvement and consumes more computational resource, the reviewer wonders the explanation of the importance and the necessity to design deep models. 2. The experimental results are not significantly improved compared with GRAND. For example, GRAND++-l on Cora with T=128 in Table 1, on Computers with T=16,32 in Table 2. Since the author claims that GRAND suffers from the over-smoothing issue while DeepGRAND significantly mitigates such issue, how to explain the differences between the theoretical and practical results, why GRAND performs better when T is larger? Besides, in Table 3, DeepGRAND could not achieve the best performance with 1/2 labeled on Citeseer, Pubmed, Computers and CoauthorCS dataset, which could not support the argument that DeepGRAND is more resilient under limited labeled training data. 3. Insufficient ablation study on \alpha. \alpha is only set to 1e-4, 1e-1, 5e-1 in section 5.4 with a large gap between 1e-4 and 1e-1. The author is recommended to provide more values of \alpha, at least 1e-2 and 1e-3. 4. Minor issues. The x label of Figure 2, Depth (T) rather than Time (T).
3. Insufficient ablation study on \alpha. \alpha is only set to 1e-4, 1e-1, 5e-1 in section 5.4 with a large gap between 1e-4 and 1e-1. The author is recommended to provide more values of \alpha, at least 1e-2 and 1e-3.