Recently, graph convolutional network (GCN) has been widely used for semi-supervised classification and deep feature representation on graph-structured data. However, existing GCN generally fails to consider the local invariance constraint in learning and representation process. That is, if two data points Xi and Xj are close in the intrinsic geometry of the data distribution, then their labels/representations should also be close to each other. This is known as local invariance assumption which plays an essential role in the development of various kinds of traditional algorithms, such as dimensionality reduction and semi-supervised learning, in machine learning area. To overcome this limitation, we introduce a graph Laplacian GCN (gLGCN) approach for graph data representation and semi-supervised classification. The proposed gLGCN model is capable of encoding both graph structure and node features together while maintains the local invariance constraint naturally for robust data representation and semi-supervised classification. Experiments show the benefit of the benefits the proposed gLGCN network.

本文提出了一种图拉普拉斯图卷积网络(gLGCN)方法，该方法通过编码图结构和节点特征同时保持局部不变性约束，用于图数据表示和半监督分类，并在实验中证明了其有效性。

半监督学习的图拉普拉斯正则化图卷积网络