  Graph neural networks, a powerful deep learning tool to model
graph-structured data, have demonstrated remarkable performance on numerous
graph learning tasks. To address the data noise and data scarcity issues in
deep graph learning, the research on graph data augmentation has intensified
lately. However, conventional data augmentation methods can hardly handle
graph-structured data which is defined in non-Euclidean space with
multi-modality. In this survey, we formally formulate the problem of graph data
augmentation and further review the representative techniques and their
applications in different deep graph learning problems. Specifically, we first
propose a taxonomy for graph data augmentation techniques and then provide a
structured review by categorizing the related work based on the augmented
information modalities. Moreover, we summarize the applications of graph data
augmentation in two representative problems in data-centric deep graph
learning: (1) reliable graph learning which focuses on enhancing the utility of
input graph as well as the model capacity via graph data augmentation; and (2)
low-resource graph learning which targets on enlarging the labeled training
data scale through graph data augmentation. For each problem, we also provide a
hierarchical problem taxonomy and review the existing literature related to
graph data augmentation. Finally, we point out promising research directions
and the challenges in future research.
