Paper:
Many variants of Graph Convolutional Networks (GCNs) for representation learning have been proposed recently and have achieved fruitful results in various domains. Among them, spectral-based GCNs are constructed via convolution theorem upon a theoretical foundation from the perspective of Graph Signal Processing (GSP). However, despite most of them implicitly act as low-pass filters that generate smooth representations for each node, there is limited development on the full usage of underlying information from low-frequency components. Here, we first introduce the octave convolution on graphs in spectral domain. Accordingly, we present Octave Graph Convolutional Network (OctGCN), a novel architecture that learns representations for different frequency components regarding weighted filters and graph wavelets bases. We empirically validate the importance of lowfrequency components in graph signals on semi-supervised node classification and demonstrate that our model achieves state-of-the-art performance in comparison with both spectral-based and spatial-based baselines.

Review:
This paper extends the previous graph wavelet neural network with separate computation for low frequency and high frequency part. The idea is to bring the octave convolution in vision to the graph domain. Experiments on three benchmark node classification tasks show comparable performances as previous methods. 
Overall the paper is written in a coherent and self-contained way, where the paper clearly states the related work and the contribution of this newly proposed work. Also it is interesting to see that normalizing the diagonal filter, tying the weights for low/high frequency parts would make the generalization better. However, there are several major concerns with the paper:
1. The contribution is somewhat limited. The main component is based on GWNN. While the GWNN itself takes the full spectrum of basis already, the formulation in (8) and (9) should somehow capture the same information. I think the paper spends too much content on the reviews, while lacks the intuition or theoretical explanations of the proposed formulation. 
2. Having marginal improvement on the three benchmarks is not that interesting. Also given the results are mixed with other baselines, I think more experiments (e.g., on large graphs, or graph-level supervised tasks, etc.) are necessary to demonstrate the empirical gain using the proposed formulation. 
3. Regarding the experiments, is it true that d/n=0 and d/n=1 should have exactly the same results? 
4. In Figure 2, I guess d/n=0 should be reduced to the GWNN. But it seems the performance is different than GWNN on citeseer and cora. Why there’s such inconsistency, or did I miss anything?