Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: This paper proposes an adaptive deep convolutional network (ADGCN) architecture to alleviate the performance degradation of graph convolutional networks (GCNs) as the number of layers increases. ADGCNs can be adaptively tuned to different graph structure data. The experiments are conducted on some real datasets.
strengths_and_weaknesses: Strength:

•	The idea of eliminating the equivalence between model degradation and model over-smoothing is interesting and promising.

•	The submission follows the basic requirements of format and page count. Follow the ICLR code of conduct.

Weakness:

•	Although many experimental results are provided in the paper, the experimental results need to be sharper to highlight the effect. 

suggested_changes: •	The motivation and research questions of this paper are strong and important. My main concern is that the technical contribution may not be significant.

•	Comparative experiments of the framework proposed in the paper and the effect of the architecture modified with manual parameters can be added.
review_title: null

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: This paper attempt to addresses the problem of performance degradation in GCNs as the number of layers increases. The proposed method introduce three adaptive parameters to realize a Deep GCN architecture.
strengths_and_weaknesses: [Advantages]:
- This paper is well-written and easy to follow.
- The experimental results demonstrate the potential of the proposed method.


[Disadvantages]:
- Overall, the work simply combines GCNII and DGCN. The author should provide more insights on the shortcomings of previous methods.
- The three adaptive parameters are not orthogonal, where the author should explain more about their relationship. Which one alleviate the over-smoothing problem? Which one alleviate the gradient vanishing problem? 
suggested_changes: - The author should conduct ablation experiments for each adaptive parameter and analyze their respective effects.
review_title: null

Decision: Invite to present