  Graph Neural Network (GNNs) based methods have recently become a popular tool
to deal with graph data because of their ability to incorporate structural
information. The only hurdle in the performance of GNNs is the lack of labeled
data. Data Augmentation techniques for images and text data can not be used for
graph data because of the complex and non-euclidean structure of graph data.
This gap has forced researchers to shift their focus towards the development of
data augmentation techniques for graph data. Most of the proposed Graph Data
Augmentation (GDA) techniques are task-specific. In this paper, we survey the
existing GDA techniques based on different graph tasks. This survey not only
provides a reference to the research community of GDA but also provides the
necessary information to the researchers of other domains.
