GraphixMatch: Improving semi-supervised learning for graph classification with FixMatch

Abstract

Graph classification is a critical problem that predicts the properties of an entire graph representing complicated relationships. This problem has been extensively studied in a supervised manner in various industries via graph convolutional networks that required large numbers of labeled graphs. However, labeled graphs are typically expensive and time-consuming to obtain in the real-world. Training graph classification models is difficult because of the sparseness of labeled data. Therefore, semi-supervised learning (SSL) algorithms that make full use of unlabeled data and overcome the lack of labels have led to the expansion of graph classification studies. In this study, we propose GraphixMatch to learn the graph properties of both labeled and unlabeled graphs based on the use of FixMatch for graph classification. To effectively capture the characteristics of a graph, we newly define and apply weak and strong augmentation of the graph. We evaluate the proposed method on seven benchmarks from a public dataset. We show that compared with various SSL methods, the simplified proposed method with weak and strong augmentation is superior. Furthermore, we conduct extensive additional experiments to derive the important parameters of GraphixMatch.