Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This paper presents a label-invariant augmentation for graph-structured data. It conducts the augmentation in the representation space and augments the most difficult sample while keeping the label of the augmented sample the same as the original sample. Experimental results on eight benchmark graph-structured data show that the developed metho outperforms classical GNN-based methods and recent graph contrastive learning. 
strengths_and_weaknesses: Strengths:
a. This work presents a label-invariant augmentation strategy for graph contrastive learning by involving labels in the downstream task to guide the contrastive augmentation. 
b. The authors present a lightweight technique to randomly generate a set of qualified candidates and selecting the most difficult one for increasing the model generalization.
c. Experimental results on eight graph benchmark datasets have verified the effectiveness of the developed label-invariant augmentation.
Weaknesses:
a. It is unclear why selecting the hardest augmented sample can enhance the performance. Why selecting top-K hardest augmented samples?
b. Failure cases of the develop method are not discussed.
c. As shown in Table 2, the results of the developed method are not always best among different datasets. Please explain such results.

questions: a. The model sizes and inference time of different methods in Table 2 are not discussed.
b. In Figure 2, it is unclear whether the number of augmented representations affect the final result.

limitations: The authors have discussed the limitation and potential negative societal impact.
review_title: null

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: The authors propose a graph contrastive learning approach in which the augmentations are done in feature space such that they do not change the predicted label. They present experiments on  8 different benchmarks, and show competitive results.
strengths_and_weaknesses: **Strength**:
the idea is clever and well-motivated
the experiments are extensive and rather convincing
the article is clear and well written, even if some details are sometimes missing and could be more rigorous

**Weaknesses**:
- Some parts of the paper are unclear:
    - Since picking positive examples requires the labels (to ensure label invariance and "difficulty"), how are the unlabelled graph handled?
    - it is unclear how " closest to the decision boundary of the classifier" exactly translates here
- The authors keep referring to their "hard positive mining" strategy as "finding the hardest example" or "the hardest direction", but they simply pick a hard example within a sampled set, which is very standard and do not need to be presented as an approximate bilevel optimization scheme.

**Overall**:
An interesting and well-executed idea. The article is sometimes not as clear as it could be, hiding actual crucial information. I would be inclined to increase my rating if the authors could answer my doubts convincingly and update the paper.

**Post rebuttal**
Rebuttal read and acknowledged, score updated 
questions: I would like to see the questions in my first "weakness" comment adressed and explained
limitations: N/A
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This paper proposes a label-invariant augmentation technique for graph-structured data. Different from the conventional node/edge modification and subgraph extraction, the authors conduct the augmentation in the representation space and generate the augmented samples in the most difficult direction while keeping the label of augmented data the same as the original samples. The experimental results demonstrate the effectiveness of the proposed augmentation strategy.
strengths_and_weaknesses: Pros:
1. I think the high-level idea is interesting and reasonable.
2. The paper is generally written in a clear way.
3. The empirical studies are quite convincing.


Cons:
1. It would be better if some theoretical evidence can be provided to justify the correctness of the proposed method.
2. Some details of model need further explanations.
3. It seems that the proposed method has some technical issues.
questions: 1. In fact, many existing graph contrastive learning methods are also applicable to node classification on one graph. Can the proposed method be generalized to such problem?
2. The authors claim that they developed label-invariant augmentation strategy for graph contrastive learning which involves labels in the downstream task. I want to know the difference/relationship of this problem setting with supervised contrastive learning. Note that the label information can also be employed in supervised contrastive learning which does not involve the downstream classification task.
3. To present the motivation in Section 3.1, the authors use Fig. 1 with label rate being 50% to show that some combinations of existing augmentation strategies may even hurt the performance. I'm a bit concerned with the label rate 50%, as this is too high for semi-supervised learning. Maybe, if the label rate is low (e.g., 5%-10%), the advantage of some augmentations will become prominent.
4. For the part of "Label-invariant Augmentation" on Page 5, I do not think d defined in Eq. 4 represents direction. Note that this is simply the distance between the representation of a graph to the centroid (like ||a-b||^2), rather than direction vector like a-b. The \delta is simply a random vector, which is also not a direction vector. Therefore, I cannot fully understand the authors' claim that they "augment samples in most difficult direction while keeping the label of augmented data the same as the original samples." Moreover, how to keep the label of augmented data the same as the original samples?? I also did not find specific explanations on this.
5. In experiments, the authors use 30%, 50% and 70% label rates, which I also think is too high. Note that the definition of semi-supervised learning is that we only have a handful of labeled data in addition to a large amount of unlabeled data.
limitations: 1. The model has some mathematical errors.
2. The label rates adopted in this paper are too high for semi-supervised learning.
review_title: null

Meta-review: metareview: The paper finally received three unanimous scores 6/6/7 and all the reviewers think the authors have addressed their initial concerns. The AC also think positive to this work and suggst to accept this paper.
confidence: Certain
Decision: Accept