In this paper, we propose the G raph Learning D ual G raph Convolutional Neural Network called GLDGCN based on the classical Graph Convolutional Neural Network 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 graph data during the experiment, we also introduce subgraph clustering and stochastic gradient descent technology into GCN and design a semi-supervised node classification algorithm based on the CLustering G raph Convolutional neural Network, which enables GCN to process large graph and improves its application value. We complete semi-supervised node classification experiments on two classical large graph which are PPI data sets (more than 50,000 nodes) and Reddit data sets (more than 200,000 nodes), and also perform well.

我们提出了基于经典的图卷积神经网络的图学习双图卷积神经网络GLDGCN，并将其应用于半监督节点分类任务，在Citeseer、Cora和Pubmed三个引文网络上取得了更高的分类准确率，同时分析和讨论了超参数和网络深度的选择。我们还将子图聚类和随机梯度下降技术引入GCN，并设计了基于聚类图卷积神经网络的半监督节点分类算法，使GCN能够处理大规模图数据并提高其应用价值，在PPI数据集（超过50,000个节点）和Reddit数据集（超过200,000个节点）上也表现良好。

基于图学习的双图卷积网络用于半监督节点分类与子图草图