Paper:
Graph Neural Networks as a combination of Graph Signal Processing and Deep Convolutional Networks shows great power in pattern recognition in nonEuclidean domains. In this paper, we propose a new method to deploy two pipelines based on the duality of a graph to improve accuracy. By exploring the primal graph and its dual graph where nodes and edges can be treated as one another, we have exploited the benefits of both vertex features and edge features. As a result, we have arrived at a framework that has great potential in both semisupervised and unsupervised learning.

Review:
This paper utilizes two pipelines based on the duality of a graph and the primal graph for semi-supervise learning. More specifically, the authors transform the primal graph into its dual form and build a two-pipelines architecture to learn on the primal graph and dual graph together. The goal of including a dual pipeline is to use the predictions on the dual node to affect the predictions of the primal nodes. 
I decided to give a weak reject to this paper for the following shortcomings:
1. Novelty is not enough. Dual graphs are explored utilized to graph neural networks in some related works (such as [1]). This paper utilizes dual graphs to affect or assist the prediction on the primal nodes, which is very similar to the methods used in [1]. 
2. Experiments can not prove the effectiveness of this method very well. The experiments only conduct on 3 datasets and the analysis of the experimental results is not convincing and more details should be included. For example, if GCN(double-pipeline) performs better than GCN, this can somehow support the effectiveness of a double-pipeline that includes a dual graph. However, the results of TwinGCN can not support the effectiveness of regularization by KL-divergence. I think more experiments and analyses about why this method works should be explored in Section Experiments.
3. The organization should be improved. In general, when introducing a model, it is better to introduce the prediction part of the model first. For example, I can get the idea of how to train the DualGCN by reading the Section Method. However, I have no idea about how the authors make a prediction on the trained DualGCN, how to transform the prediction of the dual graph to the prediction of primal nodes. I think these details are significant for this paper. Also, there are several incomplete sentences in this paper.
Overall, this idea that utilizes the dual graph to assist the primal graph learning is good and can be explored in the future. For the current version, I give a weak reject for the above reasons.

Reference
[1] Chen et al. Supervised community detection with line graph neural networks.