Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Great Start (GS): a submission which meets some of the reviewing criteria but has room for improvement
review_content: summary_of_contributions: This work introduces GAT-style attention into a Variational Graph Autoencoder (method is called AVGAE) as opposed to the standard convolutional approach. Method optimises standard ELBO while outperforming GAE and vanilla VGAE on Cora and Citeseer.
strengths_and_weaknesses: Hello authors,

Thank you for choosing to participate in Tiny Papers this year! Here are some comments:

**STRENGTHS**
- Clear-cut explanation of the method and what it brings to the table. While not novel, the work provides a systematic recount of the different moving parts involved.
- Evident that the method introduced (AVGAE) outperforms its no-attention counterparts.

**WEAKNESSES**
- Cora and Citeseer, despite being in the community for so long, are not great benchmark datasets as they lack the problem complexity needed to truly evaluate GNN performance with [[https://arxiv.org/abs/2003.00982](https://arxiv.org/abs/2003.00982)]. Using them as the target datasets doesn't guarantee true, objective improvement given their simplicity.
suggested_changes: Hello authors, good work on the paper. Here are some pressing changes and good-to-haves to solidify the paper:

**URGENT**
- Equation (3) seems to be unformatted. I was able to make out what it means because of Equation (4).
- There was no explanation given as to why you used incomplete versions of the datasets (page 2, paragraph below table) or why certain edges in the citation graphs were removed. A short line or two justifying this design choice would be good. 
- Typo: "the" instead of "he" in the Training Objective second paragraph ("Here KL[.|.] denotes **the** Kullback-Leibler ...").
- I would suggest benchmarking on high-quality datasets such as ZINC, MNIST, PATTERN, and CLUSTER. You can find them here: [https://github.com/graphdeeplearning/benchmarking-gnns](https://github.com/graphdeeplearning/benchmarking-gnns).

**GOOD-TO-HAVE**
- Ablate how # of heads affects performance (more attention = better AP?)
review_title: null

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: The paper proposes Attention Based Variational Graph Auto-Encoder (AVGAE). AVGAW involves two improvements compared to the conventional Graph Auto-Encoder  which are using probabilistic encoder (from VAE) and using attention architecture as replacement for the conventional fully connected architecture.
strengths_and_weaknesses: # Strengths
- The paper is new in terms of applying probabilistic encoder and attention architecture to GAE.
- Experiments show the benefit of the proposed AVGAE compared to the GAW and the VGAE.

# Weaknesses 
- The mathematical derivation should be written in more detail instead of high-level equations e.g., parameterization trick, Jensen inequality
suggested_changes: The mathematical derivation should be written in more detail instead of high-level equations e.g., parameterization trick, Jensen inequality.
review_title: null

Decision: Invite to archive