Graph Augmentation Learning
It provides a comprehensive guideline for scholars to choose optimal graph augmentation strategies, addressing a black-box problem in graph-based applications.
This survey tackles the lack of systematic understanding and guidelines for Graph Augmentation Learning (GAL) methods, which are used to handle incomplete or noisy graph data in applications like social network analysis and traffic forecasting, by reviewing techniques across macro, meso, and micro levels and experimentally validating their effectiveness in downstream tasks.
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.