Graph manifold learning with non-gradient decision layer

Abstract

Generally, Graph convolution network (GCN) utilizes the graph convolution operators and the softmax to extract the deep representation and make the prediction, respectively. Although GCN successfully represents the connectivity relationship among the nodes by aggregating the information on the graph, the softmax-based decision layer may result in suboptimal performance in semi-supervised learning with less label support due to ignoring the inner distribution of the graph nodes. Besides, the gradient descent will take thousands of interaction for optimization. To address the referred issues, we propose a novel graph deep model with a non-gradient decision layer for graph mining. Firstly, manifold learning is unified with label local-structure preservation to capture the topological information and make accurate predictions with limited label support. Moreover, it is theoretically proven to have analytical solutions and acts as a non-gradient decision layer in graph convolution networks. Particularly, a joint optimization method is designed for this graph model, which extremely accelerates the convergence of the model. Finally, extensive experiments show that the proposed model has achieved excellent performance compared to the current models.