NCCR: Neighbor and Cluster Consistency Regularization for Improving Graph Node Classification

Abstract

Semi-supervised node classification in graphs is a key problem in machine learning, and graph neural networks (GNNs) currently achieve state-of-the-art performance. However, traditional GNNs fail to make use of a substantial amount of information available in a graph. For example, the training loss is often defined only with respect to labeled training nodes, which usually make up a small proportion of the graph. Also, most graphs exhibit a certain degree of homophily, in which neighboring nodes are likely to belong to the same class, but GNNs typically do not make use of this property in an explicit way. In this work, we introduce a new type of consistency regularization which is able to make use of data from unlabeled nodes and also exploits graph homophily in a novel and more accurate way. Additionally, we observe that nodes in a graph may exhibit different amounts of homophily, so that uniformly enforcing neighbor consistency regularization across all nodes can reduce accuracy. We thus introduce a second clustering based regularization targeting low homophily nodes which lack reliable information from their neighbors. We show that we can flexibly combine the two regularizations with existing GNN backbones, and then demonstrate the effectiveness of the combined method by achieving state-of-the-art accuracy on a number of datasets.