Venue: ICLR 2020 Conference

Reviews:

final_score: rating: 6: Weak Accept
review_content: Scaling GCNs to large graphs is important for real applications. Instead of sampling the nodes or edges across GCN layers,  this paper proposes to sample the training graph to improve training efficiency and accuracy. It is a smart idea to construct a complete GCN from the sampled subgraphs.  Convincing experiments can verify the effectiveness of the proposed method.  It is a good work.

Question: 
1. How can the authors guarantee that subgraphs are properly sampled? Are there any  theoretical guarantee?  

review_title: Official Blind Review #2

final_score: rating: 6: Weak Accept
review_content: This paper proposes a training method for graph convolution networks on large graphs. The idea is to train a full GCN on partial samples of the graph. The graph samples are computed based on the graph connectivity, and the authors propose methods for reducing the bias and variance in the training procedure. 

The idea is elegant and intuitive, and the fact that the approach can work with various graph sampling methods adds to its generality. The paper is well-written and the fact that code is published is valuable.

The results on bias and variance are under the assumption that each layer independently learns an embedding. This would be clearer if added explicitly in the theorem statements (and not as part of the main text). It would be interesting to discuss how realistic this assumption is, and how large the actual bias is. Perhaps this can be measured empirically? 
Nevertheless, the empirical result indeed support the claim that this simplifying assumption is enough to derive useful learning rules.

Overall, I believe this is a solid contribution, and I can foresee future extensions that improve the results with more complex graph sampling methods.

Question to the authors: I did not understand the second equality in Eq. 3. Could there be a typo?

review_title: Official Blind Review #3

final_score: rating: 6: Weak Accept
review_content: This paper proposed a new sampling method to train GCN in the mini-batch manner. In particular, unlike existing methods which samples the mini-batch in the node-wise way, GraphSAINT proposed to sample a mini-batch in the graph-wise way. As a result, GraphSAINT uses the same graph across different GCN layers, while most existing methods use different graphs across different GCN layers.  In addition, the authors show that this sampling method is unbiased. Extensive experimental results have shown improvement over existing methods. Overall, this idea is interesting and well presented. 

Pros:
1. A new sampling method for the stochastic training of GCN. Have good performance.
2. Extensive experiments to verify the performance of the proposed method.
3. The theoretical analysis looks sound.

Cons:
1. GraphSAGE and FastGCN use different graphs across different GCN layers, while ClusterGCN and GraphSAINT use the same graph across different GCN layers. To make a fair comparison, it is necessary to have the same batch size for different methods. How do you deal with this issue in your experiment?
2. For ClusterGCN, the clustering procedure is done before the training. So, it needs much less computational overhead for sampling in the training course. However, GraphSAINT needs to do the heavy sampling online. Thus, it may consume more time than ClusterGCN for large graphs. It's better to show the running time of these two methods. 

review_title: Official Blind Review #1

Meta-review: All three reviewers advocated acceptance. The AC agrees, feeling the paper is interesting. 
Decision: Accept (Poster)