# Null Model-Based Data Augmentation for Graph Classification

arXiv: https://arxiv.org/abs/2112.00476 · HTML: https://mlsanity.com/paper/2112.00476/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2021.

## Built on by (most cited first, 2 of 2)

- [AGSOA:Graph Neural Network Targeted Attack Based on Average Gradient and Structure Optimization](https://mlsanity.com/paper/2406.13228/) (2024)
- [Adaptive Substructure-Aware Expert Model for Molecular Property Prediction](https://mlsanity.com/paper/2504.05844/) (2025)

## Abstract (arXiv)

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.