Null Model-Based Data Augmentation for Graph Classification

Abstract

In network science, null model is typically used to generate a series of graphs based on randomization under certain condition, which is widely used as a term of comparison to verify whether the networks in question display some non-trivial features, such as community structure. Since such non-trivial features may play a significant role in graph classification, the null model could provide a new perspective for regularization, so as to lead to the enhancement of classification performance. In this paper, we propose a novel data augmentation framework based on null model for graph classification, which contains four parts: feature ranking, graph data augmentation, data filtration, and model retraining. Moreover, in this framework, three heuristic null model generation methods are proposed for different features. Experiments are conducted on five famous benchmark datasets, and the results show that our framework has promising performance, providing a new direction of data augmentation for graph classification.