  In recent years, graph representation learning has achieved remarkable
success while suffering from low-quality data problems. As a mature technology
to improve data quality in computer vision, data augmentation has also
attracted increasing attention in graph domain. For promoting the development
of this emerging research direction, in this survey, we comprehensively review
and summarize the existing graph data augmentation (GDAug) techniques.
Specifically, we first summarize a variety of feasible taxonomies, and then
classify existing GDAug studies based on fine-grained graph elements.
Furthermore, for each type of GDAug technique, we formalize the general
definition, discuss the technical details, and give schematic illustration. In
addition, we also summarize common performance metrics and specific design
metrics for constructing a GDAug evaluation system. Finally, we summarize the
applications of GDAug from both data and model levels, as well as future
directions. Latest advances in GDAug are summarized in a GitHub repository:
https://github.com/jjzhou012/GDAug-Survey.

Machine Learning