Venue: ICLR 2022 Conference

Reviews:

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: This paper proposed adversarial training for few-shot link prediction problems. The authors introduce a domain discriminator on pairs
of graph-level embedding and address the issue of few-shot learning in graph data. The proposed method is tested on 3 benchmark datasets and achieves good performance compared to the prior approaches.
main_review: #### Strength

- The paper first proposed a few-shot link problem with imbalanced domains.
- The proposed method achieves good performance compared to baselines and the authors use t-SNE plots as a rule of thumb for practitioners to decide whether to incorporate the proposed method.
- Visual explanations of the source of improvements also validate the proposed approach.

#### Weakness

- Use adversarial training in graph or link prediction is not novel. There is a lot of prior work which is not discussed in the paper. 

[1] Lei, K., Qin, M., Bai, B., Zhang, G., and Yang, M., 2019, April. Gcn-gan: A non-linear temporal link prediction model for weighted dynamic networks. In IEEE INFOCOM 2019-IEEE Conference on Computer Communications (pp. 388-396). IEEE.
[2] Wang, H., Wang, J., Wang, J., Zhao, M., Zhang, W., Zhang, F., Xie, X. and Guo, M., 2018, April. Graphgan: Graph representation learning with generative adversarial nets. In Proceedings of the AAAI conference on artificial intelligence (Vol. 32, No. 1).

- The proposed few-shot link prediction setting is kind of arbitrary, what is the application and motivation behind this setting? why the last domain has to be 1 shot? 

- The model is a three layers GCN with 32 neurons. there are no ablations for the hyper-parameters for the model. 
summary_of_the_review: The paper proposed an interesting few-shot link prediction problem. However, important related work is not discussed in the paper. More work should stress the difference between the proposed method and prior work.
review_title: null

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: This paper presents a novel approach to  link prediction methods in graph representation of imbalanced domains using adversarial training. the result is an improved domain-agnostic graph embeddings. The approach takes is similar to few-shot learning that is popular in computer vision. Discussions on how to define shots for graphs, and how to design experiments for addressing imbalanced domains contribute to the novelty of the paper. Results on two Stanford Open Graph Benchmarks and the PPI dataset are given.

main_review: Strengths: Application of few shot learning methodology to predicting graph links is novel. Will have applications beyond the benchmark datasets used in the paper.
Weaknesses: I like this paper and so do not see any obvious weaknesses.
summary_of_the_review: A novel application of few shot learning and domain adaptation to machine learning from graph representations.
review_title: null

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: The paper proposes a method to resolve the issue of domain imbalanced dataset in graph link prediction. The proposed method uses adversarial training to generate graph embeddings that are domain agnostic in order to facilitate transfer learning cross domains. The paper uses T-SNE plot of graph embedding to gain insights of the best scenarios for applying the proposed methods. The paper compares the proposed method with heuristics-based and GNN-based domain adaptation methods using experiments.

main_review: Strength
The proposed domain adaptive method of using adversarial training is well described.
The experimental results support the effectiveness of the proposed method.
The T-SNE plots add good insights of the proposed method, can facilitate its applicability.

Weakness
The paper hasn’t discussed the key difference between the proposed method and the DANN (Domain-Adversarial NN). It made it harder to assess the novelty of the proposed method. The paper seems like a direct application of DANN on a new problem, link prediction using GNN.

The paper’s experimental settings might not be close to a real world scenario. The paper selects 10 samples for other domains and only 1 sample for an imbalanced domain. How likely is this a real world scenario? This simple scenario makes it hard to justify the universal applicability of the proposed method.

summary_of_the_review: The paper should clearly claim its novelty and run more experiments that are closer to real-world scenarios. I am going with a rating to marginally reject the paper, but willing to change the rating if authors can address the above concerns.
review_title: null

final_score: rating: 3: reject, not good enough
review_content: summary_of_the_paper: This paper proposes a method for few-shot graph link prediction by using a domain discriminator. The motivation is to learn domain-invariant graph-level representations. The authors also introduce the concept of “shot” in the graph. The proposed method outperforms baselines on three different datasets.
main_review: Strength:

This paper introduces adversarial learning (which is widely used in UDA methods) to few-shot graph link prediction problems to learn domain-invariant representations.

Weakness: 
1.	The motivation of this work should be further justified. In few-shot learning, we usually consider how to leverage a few instances to learn a generalizable model. This paper defines and creates a few-shot situation for graph link prediction, but the proposed method does not consider how to effectively use “few-shot” and how to guarantee the trained model can be generalized well to new tasks with 0/few training steps. 
2.	The definition of “domain” in this paper is unclear. For instance, why select multiple domains from the same single graph in ogbn-products? Should we consider the selected domains as “different domains”?
3.	The application of adversarial learning in few-shot learning is confusing. Adversarial learning in domain adaptation aims to learn domain-invariant representations, but why do we need such kind of representation in few-shot learning?

summary_of_the_review: This paper is not well motivated and needs further justification.
review_title: null

Meta-review: This paper has been reviewed by four expert reviewers who gave diverging scores. The three negative reviewers have provided significant constructive feedback. The main criticism is the lack of novelty and clarity in the paper. The authors have submitted their rebuttal which did not improve the scores of these reviewers. After the discussion phase, the paper did not obtain any support for acceptance and stayed under the acceptance threshold. Following the reviewers' recommendation, the meta reviewer recommends rejection.
Decision: Reject