Paper:
Published as a conference paper at ICLR 2018 FASTGCN: FAST LEARNING WITH GRAPH CONVOLU- TIONAL NETWORKS VIA IMPORTANCE SAMPLING
The graph convolutional networks (GCN) recently proposed by Kipf and Welling are an effective graph model for semi-supervised learning. This model, however, was originally designed to be learned with the presence of both training and test data. Moreover, the recursive neighborhood expansion across layers poses time and memory challenges for training with large, dense graphs. To relax the requirement of simultaneous availability of test data, we interpret graph convolutions as integral transforms of embedding functions under probability measures. Such an interpretation allows for the use of Monte Carlo approaches to consistently estimate the integrals, which in turn leads to a batched training scheme as we propose in this work-FastGCN. Enhanced with importance sampling, FastGCN not only is efficient for training but also generalizes well for inference. We show a comprehensive set of experiments to demonstrate its effectiveness compared with GCN and related models. In particular, training is orders of magnitude more efficient while predictions remain comparably accurate.

Review:
Update:
I have read the rebuttal and the revised manuscript. Additionally I had a brief discussion with the authors regarding some aspects of their probabilistic framework. I think that batch training of GCN is an important problem and authors have proposed an interesting solution to this problem. I appreciated all the work authors put into the revision. In this regard, I have updated my rating. However, I am not satisfied with how the probabilistic problem formulation was presented in the paper. I would appreciate if authors were more upfront about the challenges of the problem they formulated and limitations of their results. I briefly summarize the key missing points below, although I acknowledge that solution to such questions is out of scope of this work.
1. Sampling of graph nodes from P is not iid. Every subsequent node can not be equal to any of the previous nodes. Hence, the distribution changes and subsequent nodes are dependent on previous ones. However, exchangeability could be a reasonable assumption to make as order (in the joint distribution) does not matter for simple choices of P. Example: let V be {1,2,3} and P a uniform distribution. First node can be any of the {1,2,3}, second node given first (suppose first node is '2') is restricted to {1,3}. There is clearly a dependency and change of distribution.
2. Theorem 1 is proven under the assumption that it is possible to sample from P and utilize Monte Carlo type argument. However, in practice, sampling is done from a uniform distribution over observed samples. Also, authors suggest that V may be infinite. Recall that for Monte Carlo type approaches to work, sampling distribution is ought to contain support of the true distribution. Observed samples (even as sample size goes to infinity) will never be able to cover an infinite V. Hence, Theorem 1 will never be applicable (for the purposes of evaluating population loss). Also note that this is different from a more classical case of continuous distributions, where sampling from a Gaussian, for instance, will cover any domain of true distribution. In the probabilistic framework defined by the authors it is impossible to cover domain of P, unless whole V is observed.
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This work addresses a major shortcoming of recently popularized GCN. That is, when the data is equipped with the graph structure, classic SGD based methods are not  straightforward to apply. Hence it is not clear how to deal with large datasets (e.g., Reddit). Proposed approach uses an adjacency based importance sampling distribution to select only a subset of nodes on each GCN layer. Resulting loss estimate is shown to be consistent and its gradient is used to perform the weight updates.
Proposed approach is interesting and the direction of the work is important given recent popularity of the GCN. Nonetheless I have two major question and would be happy to revisit my score if at least one is addressed.
Theory:
SGD requires an unbiased estimate of the gradient to converge to the global optima in the convex loss case. Here, the loss estimate is shown to be consistent, but not guaranteed to be unbiased and nothing is said about the gradient in Algorithm 1. Could you please provide some intuition about the gradient estimate? I might not be familiar with some relevant results, but it appears to me that Algorithm 1 will not converge to the same solution as full data GD would.
Practice:
Per batch timings in Fig. 3 are not enough to argue that the method is faster as it might have poor convergence properties overall. Could you please show the train/test accuracies against training time for all compared methods?
Some other concerns and questions:
- It is not quite cleat what P is. You defined it as distribution over vertices of some (potentially infinite) population graph. Later on, sampling from P becomes equivalent to uniform sampling over the observed nodes. I don't see how you can define P over anything outside of the training nodes (without defining loss on the unobserved data), as then you would be sampling from a distribution with 0 mass on the parts of the support of P, and this would break the Monte Carlo assumptions.
- Weights disappeared in the majority of the analysis. Could you please make the representation more consistent.
- a(v,u) in Eq. 2 and A(v,u) in Eq. 5 are not defined. Do they both correspond to entries of the (normalized) adjacency?