Null Model-Based Data Augmentation for Graph Classification

TL;DR

This paper introduces a novel null model-based data augmentation approach for graph classification, demonstrating significant performance improvements and highlighting the importance of feature selection in augmentation effectiveness.

Contribution

The paper proposes four standard and four approximate null model-based augmentation methods, advancing graph classification through innovative data augmentation techniques.

Findings

Standard null model-based methods outperform approximate ones.

Augmentation techniques significantly improve classification accuracy.

Choice of features impacts augmentation success across network types.

Abstract

In network science, the null model is typically used to generate a series of graphs based on randomization as a term of comparison to verify whether a network in question displays some non-trivial features such as community structure. Since such non-trivial features play a significant role in graph classification, the null model could be useful for network data augmentation to enhance classification performance. In this paper, we propose a novel technique that combines the null model with data augmentation for graph classification. Moreover, we propose four standard null model-based augmentation methods and four approximate null model-based augmentation methods to verify and improve the performance of our graph classification technique. Our experiments demonstrate that the proposed augmentation technique has significantly achieved general improvement on the tested datasets. In addition, we find that the standard null model-based augmentation methods always outperform the approximate ones, depending on the design mechanisms of the null models. Our results indicate that the choice of non-trivial features is significant for increasing the performance of augmentation models for different network structures, which also provides a new perspective of data augmentation for studying various graph classification methods.