  Graph Augmentation Learning (GAL) provides outstanding solutions for graph
learning in handling incomplete data, noise data, etc. Numerous GAL methods
have been proposed for graph-based applications such as social network analysis
and traffic flow forecasting. However, the underlying reasons for the
effectiveness of these GAL methods are still unclear. As a consequence, how to
choose optimal graph augmentation strategy for a certain application scenario
is still in black box. There is a lack of systematic, comprehensive, and
experimentally validated guideline of GAL for scholars. Therefore, in this
survey, we in-depth review GAL techniques from macro (graph), meso (subgraph),
and micro (node/edge) levels. We further detailedly illustrate how GAL enhance
the data quality and the model performance. The aggregation mechanism of
augmentation strategies and graph learning models are also discussed by
different application scenarios, i.e., data-specific, model-specific, and
hybrid scenarios. To better show the outperformance of GAL, we experimentally
validate the effectiveness and adaptability of different GAL strategies in
different downstream tasks. Finally, we share our insights on several open
issues of GAL, including heterogeneity, spatio-temporal dynamics, scalability,
and generalization.
