GRNN: Graph-Retraining Neural Network for Semi-Supervised Node Classification

Abstract

In recent years, graph neural networks (GNNs) have played an important role in graph representation learning and have successfully achieved excellent results in semi-supervised classification. However, these GNNs often neglect the global smoothing of the graph because the global smoothing of the graph is incompatible with node classification. Specifically, a cluster of nodes in the graph often has a small number of other classes of nodes. To address this issue, we propose a graph-retraining neural network (GRNN) model that performs smoothing over the graph by alternating between a learning procedure and an inference procedure, based on the key idea of the expectation-maximum algorithm. Moreover, the global smoothing error is combined with the cross-entropy error to form the loss function of GRNN, which effectively solves the problem. The experiments show that GRNN achieves high accuracy in the standard citation network datasets, including Cora, Citeseer, and PubMed, which proves the effectiveness of GRNN in semi-supervised node classification.