We present GraphMix, a regularized training scheme for Graph Neural Network based semi-supervised object classification, leveraging the recent advances in the regularization of classical deep neural networks. Specifically, we propose a unified approach in which we train a fully-connected network jointly with the graph neural network via parameter sharing, interpolation-based regularization and self-predicted-targets. Our proposed method is architecture agnostic in the sense that it can be applied to any variant of graph neural networks which applies a parametric transformation to the features of the graph nodes. Despite its simplicity, with GraphMix we can consistently improve results and achieve or closely match state-of-the-art performance using even simpler architectures such as Graph Convolutional Networks, across three established graph benchmarks: the Cora, Citeseer and Pubmed citation network datasets, as well as three newly proposed datasets : Cora-Full, Co-author-CS and Co-author-Physics.
Summary:
This paper presents a data augmentation procedure for semi-supervised learning on graph structured data. In general it is not clear how augmented observations can be incorporated into an existing graph while still preserving the graph structure. Instead of trying to incorporate an augmented dataset into the graph, this paper uses a seperate fully-connected network (FCN) that shares weights with a graph neural net (GNN). The final method also incorporates various components from the literature, such as Mixup and Sharpening. While the literature appears to have been adequately cited, the position of GraphMix relative to previous work could be better dilineated. In my opinion, the main contribution of this paper is the suggestion of using weight-sharing to sidestep the issue of incorporating augmented datapoints into graph-structured data. I am not sufficiently versed in the literature to assess whether the idea is sufficiently novel to warrant acceptance. The idea is simple and appears to improve the performance of Graph Convolutional Nets (GCNs), so I am leaning towards acceptance.
Rating Justification:
Efficient data-augmentation procedures are an important area of research. The relative simplicity and generality of GraphMix is appealing. I would give the paper a higher score if the authors showed that GraphMix(Graph U-net) was an improvement over Graph U-net, or if it was made more clear that some substantial benefit is derived from using Mixup features.
Additional Comments:
1. Based on the ablation study it seems that Mixup actually plays a very minor role in the overall success of the procedure. I would be curious to see the t-SNE visualization of the GraphMix(GCN) \ Mixup features in order to determine how much of the cluster separation is due to Mixup specifically. I understand that previous work has suggested that Mixup features are superior for discrimination than normal features, but in this work specifically the evidence for this assertion is fairly weak. 
2. A clearer distinction between GraphMix(GCN) and GCN (with predicted targets) would be very helpful, especially since GCN (with predicted targets) actually performs the best on the standard PubMed splits. Why were the results for GCN (with predicted targets) not included in Table 2?