Null Model-Based Data Augmentation for Graph Classification

Graph classification is an important task widely applied to biochemistry, social networks, and other fields. Since it is a data-dependent problem, insufficient training data will deteriorate the performance of graph classification models. To address this issue, various data augmentation methods have been proposed. However, most existing methods tend to destroy the topological features, leading to a negative impact on information propagation and semantics. While the null model generates new data with the same topological features as the original graph and helps capture the latent information based on topology. Hence, combining the null model with data augmentation for graph classification may be useful in helping models learn graph representations. This paper introduces a novel null model-based augmentation technique for graph classification. Specifically, four standard and four approximate null model-based augmentation methods are developed to verify the effectiveness of the technique. Experimental results on benchmark datasets demonstrate significant performance improvements with the proposed technique. Depending on the design mechanisms of the null models, standard augmentation methods outperform the approximate ones. These findings emphasize the critical role of non-trivial features in enhancing augmentation models for different network structures, providing a new perspective on data augmentation for studying graph classification methods.