Graph Data Augmentation for Graph Machine Learning: A Survey

TL;DR

This survey comprehensively reviews existing methods for graph data augmentation, categorizing approaches and highlighting challenges and future directions to enhance graph machine learning models.

Contribution

The paper provides a systematic taxonomy and overview of graph data augmentation techniques, addressing a relatively under-explored area in graph machine learning.

Findings

Introduces three taxonomies for categorizing augmentation methods

Summarizes recent advances and methodologies in graph data augmentation

Outlines unsolved challenges and future research directions

Abstract

Data augmentation has recently seen increased interest in graph machine learning given its demonstrated ability to improve model performance and generalization by added training data. Despite this recent surge, the area is still relatively under-explored, due to the challenges brought by complex, non-Euclidean structure of graph data, which limits the direct analogizing of traditional augmentation operations on other types of image, video or text data. Our work aims to give a necessary and timely overview of existing graph data augmentation methods; notably, we present a comprehensive and systematic survey of graph data augmentation approaches, summarizing the literature in a structured manner. We first introduce three different taxonomies for categorizing graph data augmentation methods from the data, task, and learning perspectives, respectively. Next, we introduce recent advances in graph data augmentation, differentiated by their methodologies and applications. We conclude by outlining currently unsolved challenges and directions for future research. Overall, our work aims to clarify the landscape of existing literature in graph data augmentation and motivates additional work in this area, providing a helpful resource for researchers and practitioners in the broader graph machine learning domain. Additionally, we provide a continuously updated reading list at https://github.com/zhao-tong/graph-data-augmentation-papers.