In this paper, we propose the Graph-Learning-Dual Graph Convolutional Neural Network called GLDGCN based on the classic Graph Convolutional Neural Network(GCN) by introducing dual convolutional layer and graph learning layer. We apply GLDGCN to the semi-supervised node classification task. Compared with the baseline methods, we achieve higher classification accuracy on three citation networks Citeseer, Cora and Pubmed, and we also analyze and discussabout selection of the hyperparameters and network depth. GLDGCN also perform well on the classic social network KarateClub and the new Wiki-CS dataset. For the insufficient ability of our algorithm to process large graphs during the experiment, we also introduce subgraph clustering and stochastic gradient descent methods into GCN and design a semi-supervised node classification algorithm based on the CLustering Graph Convolutional neural Network, which enables GCN to process large graph and improves its application value. We complete semi-supervised node classification experiments on two classic large graph which are PPI dataset (more than 50,000 nodes) and Reddit dataset (more than 200,000 nodes), and also perform well.

翻译：本文在经典图卷积神经网络基础上，通过引入双卷积层和图学习层，提出了名为GLDGCN的图学习-双图卷积神经网络。我们将GLDGCN应用于半监督节点分类任务。与基线方法相比，我们在Citeseer、Cora和Pubmed三个引文网络上取得了更高的分类精度，并分析和讨论了超参数选择及网络深度问题。GLDGCN在经典社交网络KarateClub和新版Wiki-CS数据集上也表现优异。针对算法处理大规模图能力不足的问题，我们进一步将子图聚类和随机梯度下降方法引入GCN，设计了基于聚类的图卷积神经网络半监督节点分类算法，使GCN能够处理大规模图并提升其应用价值。我们在两个经典大规模图数据集——PPI数据集（超过5万个节点）和Reddit数据集（超过20万个节点）上完成了半监督节点分类实验，同样取得了良好效果。