  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. To advance research in this
emerging direction, this survey provides a comprehensive review and summary of
existing graph data augmentation (GDAug) techniques. Specifically, this survey
first provides an overview of various feasible taxonomies and categorizes
existing GDAug studies based on multi-scale graph elements. Subsequently, for
each type of GDAug technique, this survey formalizes standardized technical
definition, discuss the technical details, and provide schematic illustration.
The survey also reviews domain-specific graph data augmentation techniques,
including those for heterogeneous graphs, temporal graphs, spatio-temporal
graphs, and hypergraphs. In addition, this survey provides a summary of
available evaluation metrics and design guidelines for graph data augmentation.
Lastly, it outlines the applications of GDAug at both the data and model
levels, discusses open issues in the field, and looks forward to future
directions. The latest advances in GDAug are summarized in GitHub.
