Graph convolutional networks (GCNs), as an extension of classic convolutional neural networks (CNNs) in graph processing, have achieved good results in completing semi-supervised learning tasks. Traditional GCNs usually use fixed graph to complete various semi-supervised classification tasks, such as chemical molecules and social networks. Graph is an important basis for the classification of GCNs model, and its quality has a large impact on the performance of the model. For low-quality input graph, the classification results of the GCNs model are often not ideal. In order to improve the classification effect of GCNs model, we propose a graph learning method to generate high-quality topological graph, which is more suitable for GCNs model classification. We use the correlation between the data to generate a data similarity matrix, and apply Laplacian rank constraint to similarity matrix, so that the number of connected components of the topological graph is consistent with the number of categories of the original data. Experimental results on 10 real datasets show that our method is better than the comparison method in classification effect.

图卷积网络（GCN）作为经典卷积神经网络（CNN）在图处理中的扩展，在完成半监督学习任务中取得了良好的效果。传统的GCN通常使用固定图来完成各种半监督的分类任务，例如化学分子和社交网络。图是GCN模型分类的重要依据，图的质量对模型的性能影响很大。对于低质量的输入图，GCN模型的分类结果通常不理想。为了提高GCN模型的分类效果，我们提出了一种图学习方法来生成高质量的拓扑图，该方法更适合于GCN模型的分类。我们使用数据之间的相关性来生成数据相似性矩阵，将拉普拉斯秩约束应用于相似度矩阵，以使拓扑图的连通部分数与原始数据的类别数一致。在10个真实数据集上的实验结果表明，我们的方法在分类效果上优于比较方法。