Venue: ICLR 2020 Conference

Reviews:

final_score: rating: 6: Weak Accept
review_content: The authors propose a simple but effective strategy that aims to alleviate not only overfitting, but also feature degradation (oversmoothing) in deep graph convolutional networks (GCNs).  Inspired by dropout in traditional MLPs and convnets, the authors clearly motivate their contribution in terms of alleviating both overfitting and oversmoothing, which are problems established both in previous literature as well as validated empirically by the authors. Ultimately, the authors provide solid empirical evidence that, while a bit heuristic, their method is effective at alleviating at least partially the issues of overfitting and oversmoothing.

I vote weak-accept in light of convincing empirical results, some theoretical exploration of the method's properties, but limited novelty.

Pros:
Simple, intuitive method
Draws from existing literature relating to dropout-like methods
Little computational overhead
Solid experimental justification
Some theoretical support for the method
Cons:
Method is somewhat heuristic
Mitigates, rather than solves, the issue of oversmoothing
Limited novelty (straightforward extension of dropout to graphs edges)
Unclear why dropping edges is "valid" augmentation

Followup-questions/areas for improving score:

It would be nice to have a principled way of choosing the dropout proportion; 0.8 is chosen somewhat arbitrarily by the authors (presumably because it generally performed well). There is at least a nice interpretation of choosing 0.5 for the dropout proportion in regular dropout (maximum regularization).

As brought up in the comments, edges to drop out to the graph's properties is an interesting direction to explore. While the authors state that they would like to keep the method simple and general, the method is ultimately devised as an adaptation of dropout to graphs, so exploiting graph-specific properties seems reasonable and a potential avenue to further improving performance. 

p2: "First, DropEdge can be considered as a data augmentation technique" Why are these augmentations valid; why should the output of the network be invariant to these augmentations? I would like to see some justification for why the proposed random modification of the graph structure is valid; intuitively, it seems like it might make the learning problem impossible in some cases.

Deeper analysis of the (more interesting, I think) layer-independent regime would be nice. (As a side-note, the name "layer-independent" for this regime is a bit confusing, as the edges dropped out *do* depend on the layer here, whereas in the "layer dependent" regime, edges dropped out do *not* depend on the layer).

Comments:
Figure 1 could probably be re-organized to better highlight the comparison between GCNs with and without DropEdge; consolidating the content into 2 figures instead of 4 might be more easily parsable. Adding figure-specific captions and defining the x axis would also be nice.

Use "reduce" in place of "retard"
p2 " With contending the scalability" improve phrasing
p2 "By recent," -> "Recently,"
p2 "difficulty on" -> "difficulty in"
p2 " deep networks lying" -> "deep networks lies"
p3 "which is a generation of the conclusion" improve phrasing
p3 " disconnected between" -> "disconnected from"
p4 "adjacent matrix" -> "adjacency matrix"
p4 "severer " -> "more severe"
p5 "but has no help" -> "but is no help"
p5 "no touch to the adjacency matrix" -> improve phrasing

review_title: Official Blind Review #4

final_score: rating: 3: Weak Reject
review_content: This paper studied the problem of "deep" GCNs where the goal is to develop training methods that can make GCN becomes deeper while maintaining good test accuracy.  The authors proposed a new method called "DropEdge", where they randomly drop out the edges of the input graphs and demonstrate in experiments that this technique can indeed boost up the testing accuracy of deep GCN compared to other baselines. 

This paper is clearly well-written and the authors conducted a comprehensive study on deep GCNs. I also like the discussion in sec 4.3 where the authors explicitly clarify what are the difference between DropEdge, Dropout and DropNode, as the other two are the methods that will pop up during reading this paper. The extensive experiment results also show that for deeper GCNs, DropEdge always win over other baselines (see Tab 1) despite most of them are marginal except the backbone being GraphSAGE on Citeseer. Can you explain why this is the case? Why other backbones seem to have similar performance  even with DropEdge (i.e. most of the accuracy increase are less than 3 %). 

Question:
1.  When looking at Tab 1, it looks like most of the time, 2-layers networks are already the best (or close to the best) and are clearly better than 32 layers. Therefore, this makes me wonder: why do we need deeper networks at all if the shallow networks can already achieve a good (almost best) performance and it is also much similar and efficient in training? Can you please clarify why do we care to train a deeper network at all under this scenario? Are there any reasons that we would like to use deeper network as opposed to shallower networks?

2. It is less clear to me regarding this sentence: "DropEdge either retards the convergence speed of over-smoothing or relieves the information loss caused by it" 

Overall, I think this paper presents an interesting study on making deeper GCNs comparable to shallow network performance, but since the the boosted performance doesn't really outperform most of the 2-layer networks,  I would like to hear the justification of why we need the deeper networks for this node classification task. 
review_title: Official Blind Review #3

final_score: rating: 3: Weak Reject
review_content: The authors propose a simple and interesting strategy, DropEdge, to alleviate the over-fitting and over-smoothing in GCN. The logic is simple and clear and the paper is well-written. 

Major concerns:
1. After randomly enforce a certain rate of edges to be zero, how to preserve properties in the original complex network, such as degree power-law distribution, communities? If it was not necessary to preserve the properties, then what information should be preserved from the original graph.
2. Randomly drop edges may result in disconnected components, how to handle disconnected components?
3. Why do the authors use dimension difference as the measure to quantitatively evaluate information loss in Thm 1. More dimension reduction does not mean more information loss.
4. As a follow-up concern for C1, graph sparsification makes more sense than DropEdge because it has clear information reserve targets while there is no target for the randomness in DropEdge.
5. In Table 1 and Fig 2, why the improvements for more layers are bigger than those of the fewer layers?
6. In Fig 2, why the trend of Reddit dataset is so different from others (the more layers the more improvements by applying DropEdge)? 
7. In Table 2, why there are the DropEdge versions for some methods while not for some other methods (e.g., FastGCN, ASGCN)? Why there is no result of GAT?

Minor:
1. Sec 3, "notation", "\mathbf{x}_n" -> "\mathbf{x}_N"
2. Eq (1), "\mathbf{h}_n^{(l+1)}" -> "\mathbf{x}_N^{(l+1)}"
3. What's C_l in the explaination under Eq(1)?
review_title: Official Blind Review #2

Meta-review: The paper proposes a very simple but thoroughly evaluated and investigated idea for improving generalization in GCNs. Though the reviews are mixed, and in the post-rebuttal discussion the two negative reviewers stuck to their ratings, the area chair feels that there are no strong grounds for rejection in the negative reviews. Accept.
Decision: Accept (Poster)