Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed graphs. To scale GCNs to large graphs, state-of-the-art methods use various layer sampling techniques to alleviate the "neighbor explosion" problem during minibatch training. We propose GraphSAINT, a graph sampling based inductive learning method that improves training efficiency and accuracy in a fundamentally different way. By changing perspective, GraphSAINT constructs minibatches by sampling the training graph, rather than the nodes or edges across GCN layers. Each iteration, a complete GCN is built from the properly sampled subgraph. Thus, we ensure fixed number of well-connected nodes in all layers. We further propose normalization technique to eliminate bias, and sampling algorithms for variance reduction. Importantly, we can decouple the sampling from the forward and backward propagation, and extend GraphSAINT with many architecture variants (e.g., graph attention, jumping connection). GraphSAINT demonstrates superior performance in both accuracy and training time on five large graphs, and achieves new state-of-the-art F1 scores for PPI (0.995) and Reddit (0.970).
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.