Oversampling-based node imbalance classification method in graph

Abstract

Graph neural networks have been widely used in many fields. Most studies are used to solve the node classification task, but most of the time the distribution of the data is unbalanced, which affects the classification accuracy of the model. In this paper, we balance the data distribution in the graph by generating new samples in the embedded space and introducing random variables to control the spatial distance between the new samples and the target samples. We also propose a framework to solve the unbalanced node classification task in the graph, and the experimental results show that the method is effective.