G-Mixup: Graph Data Augmentation for Graph Classification

TL;DR

This paper introduces G-Mixup, a novel graph data augmentation method that interpolates graphons to generate synthetic graphs, enhancing the generalization and robustness of graph neural networks.

Contribution

It proposes a new graph augmentation technique using graphon interpolation, addressing challenges of aligning and mixing different graph structures.

Findings

G-Mixup significantly improves GNN performance on classification tasks.

The method enhances model robustness against adversarial attacks.

Experimental results demonstrate better generalization compared to baseline methods.

Abstract

This work develops \emph{mixup for graph data}. Mixup has shown superiority in improving the generalization and robustness of neural networks by interpolating features and labels between two random samples. Traditionally, Mixup can work on regular, grid-like, and Euclidean data such as image or tabular data. However, it is challenging to directly adopt Mixup to augment graph data because different graphs typically: 1) have different numbers of nodes; 2) are not readily aligned; and 3) have unique typologies in non-Euclidean space. To this end, we propose $\mathcal{G}$-Mixup to augment graphs for graph classification by interpolating the generator (i.e., graphon) of different classes of graphs. Specifically, we first use graphs within the same class to estimate a graphon. Then, instead of directly manipulating graphs, we interpolate graphons of different classes in the Euclidean space to get mixed graphons, where the synthetic graphs are generated through sampling based on the mixed graphons. Extensive experiments show that $\mathcal{G}$-Mixup substantially improves the generalization and robustness of GNNs.