Venue: NeurIPS 2021 Conference

Reviews:

final_score: rating: 7: Good paper, accept
review_content: summary: The paper combines the ideas of adversarial training (on augmentation) and graph contrastive learning (branded as "AD-GCL") to further boost the unsupervised performance, targeting at representing less redundant information which is on pair with information bottleneck principle. It is empirically demonstrated with significant performance gains in multiple experiment settings of unsupervised, semi-supervised and transfer learning.
main_review: Generally good presentation and solid experiments. I like it in general, with a few comments and suggestions:

(i) (Method): Using learnable edge perturbation to parameterize augmentation is a practical way but might be restricted. Can authors provide some other instantiations to consolidate the proposed idea (e.g. learnable node masking)?

(ii) (Theoretical claim): The claim in Theorem 1 holds the assumption seemingly conflicting with the goal of unsupervised learning, that assuming the GNN is already endowed with good enough distinguishment power, while this is difficult to achieve specifically for certain downstream labels since unsupervised learning does not have labels. More clarification of this claim is needed.

(iii) (Experiment): Table 1&3 show AD-GCL achieves the (nearly) best performance among all datasets (including molecules) but Table 2 shows only 3 out of 8 doing better. Is there any explanation (analysis) for these contradictory observations?
limitations_and_societal_impact: Nothing in particular
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: Current contrastive learning (CL) methods are based on various graph augmentations to generate multiple views for contrast (maximizing MI). To discover the optimal augmentations, the authors propose an adversarial approach to automatically learn views generation that tries to minimize the MI between two views.
main_review: This paper proposed a novel method leveraging adversarial training to learn augmentations for graph contrastive learning. They also introduce edge-dropping augmentation for graph-level tasks as an example to show the theoretical base of the model and the outperforming of their model compared with other popular graph contrastive methods. 

The proposed method is overall novel and interesting. However, there are major concerns in the experimental results. In Table 2, 8, 9 10, the performance gain of the proposed method is not consistent among datasets, especially when comparing to its direct baseline (GraphCL). This makes the author's claim and the proposed method less convincing. The authors may need more experiments to show the effectiveness of their methods on different tasks.

Below are additional questions in need of clearance.

1, In Section 3.2 (Learnable Edge Dropping GDA model T_\Phi()), the authors firstly claimed that “structure fingerprints” are important for downstream tasks and then claimed that dropping some edges will not change subgraph structure. The two claims seem conflicting with each other as one type of subgraph can be turned into another type of subgraph by dropping some edges, then the contrastive learning method cannot distinguish these two types of subgraphs. This situation is common in the chemical analysis of molecules, so the authors may need to introduce additional assumptions or proof of this question if they want to mainly illustrate their method by edge-dropping augmentation.

2, In Section 3.2 (Parameterizing T_\Phi()), the authors called their T as a “graph model”, this was confusing because it was not consistent with the terminology used in Section 3.1, especially in Definition 1 and Definition 2. Do the authors mean “instance of graph augmentation model”?

3, In Section 3.2 (Regularizing T_\Phi()), the authors proposed to add a regularization term to control the edge perturbation aggressiveness. This is a good idea and classic method in machine learning, but I have two detailed questions here. First, even with the regularization term, it can still have some augmented graphs without any edges in training. Is this situation reasonable or how do the authors prevent it? Second, adding this regularization term was for “not to perform very aggressive perturbation”, but in the Experiment details (Appendix F), the authors showed that aggressive perturbation was somehow better. Let us consider a situation where only the node feature suffices IB or InfoMin target, then the above situation (3.1) is reasonable and can be common sense. The author needs to make it more convincing the necessity of using the regularization term, the edge dropping method, or even GNNs in these experiments.

4, Although the authors showed that even only learnable edge dropping augmentation could lead to SOTA results, the Claims and Proofs in Section 3.1 were not limited in edge dropping and it seems not very easy to analogy edge drooping to other augmentation methods like node dropping and feature masking. The authors maybe need some brief explanation of why this method is not limited to edge-dropping augmentation and suitable tasks for edge-dropping. Also, it would be better to include more experiments to show their learnable augmentation method can be easily transferred to other augmentation methods and suitable tasks for them.

5, Typo at line 137



===================

I'd like to update my score after reading the authors' responses and comments from other reviewers.
limitations_and_societal_impact: The authors have discussed the limitations and societal impact in the Appendix.
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: In this paper, the authors try to improve the graph contrastive learning. They point out that the representation learned by maximizing mutual information does not necessarily help for downstream tasks. And they propose adversarial graph augmentation to enhance the InfoMax objective for self-supervised learning of GNNs. They argue that AD-GCL will make GNNs avoid learning the redundant information during the training, which are not beneficial to GraphCL. The author also gave a relatively sufficient theoretical explanation. Extensive experiments are conduct to demonstrate the effectiveness of the proposed method.
main_review: In general, the paper is written well and easy to follow. However, there are still several concerns that the authors need to address:

1. Novelty are limited.
(1) Recently, there have been many works [2,3] that have studied the use of high quality data augmentation on graph to improve the performance of contrast learning. The novelty of this work that only improves the performance of graph representation learning by how to drop the edges is very limited.
(2) The method proposed by the author is only to drop the edges, and does not discuss some common graph data augmentation methods, such as dropping nodes, extracting subgraphs and other diverse data augmentation methods mentioned in [1], so I think this data augmentation method is too single, too simple, and very limited in novelty.

2. Questions about the GIB and InfoMax.
(1) The example of failure given in Figure 2 is not credible, because self-supervised learning does not require label information, but it forces the use of random labels to force self-supervised learning to use wrong information while ensuring maximum mutual information. The label information destroys the learned representation. This is unlikely to happen in practical applications, or this is a very extreme situation. I hope that the author can use a more practical example to illustrate that the maximization of mutual information may not necessarily helpful for downstream tasks.
(2) I think the core contribution of this work is what the author mentioned in lines 138-140. It does not need to know the label information of downstream tasks and can capture the minimal information for each graph. And the author's proposed method does not need labels for downstream tasks, but the left and right sides of the equation in Theorem 1 both contain label information. How did the author optimize to remove the label influence.
(3) Will the adversarial data augmentation method proposed by the author have an impact on the maximization of mutual information? In other words, it is impossible to conduct contrastive learning better because of the adversarial examples. I hope the author can give a detial comparison of the accuracy of contrastive learning.

3. Questions about the data augmentation.
(1) The framework proposed by the author only uses a learning method to drop edges, but it cannot perform data augmentation similar to the other methods mentioned in [1]. 
Simply choosing which edges need to be dropped is definitely not the best choice for graph data.
For graph classification tasks, there are many nodes, node features and even some subgraphs can also produce adversarial data augmentation, but the framework only cover on a small part of the search space, which undoubtedly ignored a lot of other possibilities.
(2) As mentioned in [1], dropping edges is not effective for all datasets. For example, for some molecular data sets such as biochemistry, the connection relationship is a very important property for this kind of data. If you let GNN learn this invariance, it will have a negative impact on downstream tasks. Does the author also find this same rule in the experiment? If found, please give detailed experimental results, if not found, please explain the reasons.

4. Questions about the experiments.
(1) The paper is missing a lot of baselines[2,3], please compare your experimental results with these methods, and point out the superiority of your method and some of the shortcomings of their methods.
(2) The paper only conducted experiments on many small data sets. May I ask whether it is also effective for large data sets such as OGB datasets [4]. If so, please give more experimental results on large datasets.
(3) The paper just performed a lot of experiments on graph classification tasks. May I ask whether it is also effective for node classification or link prediction tasks. If it is effective, please provide some experimental results. If it is not effective, please give an explanation.

[1] Graph contrastive learning with augmentations

[2] Graph Contrastive Learning with Adaptive Augmentation

[3] Graph Contrastive Learning Automated

[4] https://ogb.stanford.edu/
limitations_and_societal_impact: Please refer to the above questions.
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: The paper proposes an adversarial graph augmentation technique to boost graph contrastive learning (GCL), with the purpose of avoiding capturing redundant information to be robust. The proposed technique is driven by the information bottleneck principle (IB) with certain theoretical guarantee and empirical verification.
main_review: (Originality)
The paper looks innovative to me.

(Quality)
The overall idea and presentation are clear with extensive experiments on molecule and social network datasets to support rge method.
Theorem 1 is reasonable and informative to show the bound of information the adversarial technique captures. Nevertheless, the derivation is straightforward (combination of the property of infectivity and mutual information rewriting), and therefore it is suggested to downgrade it into proposition to avoid backfire, while it still reveals the key insight of the proposed AD-GCL method.

(Optional clarification)
Would you clarify the contributions in this paper between a latest paper "[Graph Contrastive Learning Automated](https://arxiv.org/abs/2106.07594)" also leveraging adversarial training on GCL?

limitations_and_societal_impact: Not applicable.
review_title: null

Meta-review: The paper combines the ideas of adversarial training (on augmentation) and graph contrastive learning to further boost the unsupervised performance, targeting at representing less redundant information. It is empirically demonstrated with significant performance gains in multiple experiment settings of unsupervised, semi-supervised and transfer learning.

After the discussion, all reviewers agreed that this paper is above the acceptance threshold. The authors are encouraged to improve this work based on the feedback from the reviewers, especially the following ones.

1. In experiments, compare with a recent work -- "Graph Contrastive Learning Automated", which also leverages adversarial training for GCL. In addition, the authors can compare with the following two works:

     - Graph Contrastive Learning with Adaptive Augmentation

      - Graph Contrastive Learning Automated

2. The performance gain of the proposed method is not consistent among datasets, especially when comparing to its direct baseline (GraphCL). This makes the author's claim and the proposed method less convincing. The authors may need more experiments to show the effectiveness of their methods on different tasks.

Decision: Accept (Poster)