Deep graph learning for semi-supervised classification

Abstract

Graph learning (GL) can dynamically capture the distribution structure (graph structure) of data based on graph convolutional networks (GCN), and the learning quality of the graph structure directly influences GCN for semi-supervised classification. Most existing methods combine the computational layer and the related losses into GCN for exploring the global graph (measuring graph structure from all data samples) or local graph (measuring graph structure from local data samples). The global graph emphasizes the whole structure description of the inter-class data, while the local graph tends to the neighborhood structure representation of the intra-class data. However, it is difficult to simultaneously balance these learning process graphs for semi-supervised classification because of the interdependence of these graphs. To simulate the interdependence, deep graph learning (DGL) is proposed to find a better graph representation for semi-supervised classification. DGL can not only learn the global structure by the previous layer metric computation updating, but also mine the local structure by next layer local weight reassignment. Furthermore, DGL can fuse the different structures by dynamically encoding the interdependence of these structures, and deeply mine the relationship of the different structures by hierarchical progressive learning to improve the performance of semi-supervised classification. Experiments demonstrate that the DGL outperforms state-of-the-art methods on three benchmark datasets (Citeseer, Cora, and Pubmed) for citation networks and two benchmark datasets (MNIST and Cifar10) for images.