  Recent works explore learning graph representations in a self-supervised
manner. In graph contrastive learning, benchmark methods apply various graph
augmentation approaches. However, most of the augmentation methods are
non-learnable, which causes the issue of generating unbeneficial augmented
graphs. Such augmentation may degenerate the representation ability of graph
contrastive learning methods. Therefore, we motivate our method to generate
augmented graph by a learnable graph augmenter, called MEta Graph Augmentation
(MEGA). We then clarify that a "good" graph augmentation must have uniformity
at the instance-level and informativeness at the feature-level. To this end, we
propose a novel approach to learning a graph augmenter that can generate an
augmentation with uniformity and informativeness. The objective of the graph
augmenter is to promote our feature extraction network to learn a more
discriminative feature representation, which motivates us to propose a
meta-learning paradigm. Empirically, the experiments across multiple benchmark
datasets demonstrate that MEGA outperforms the state-of-the-art methods in
graph self-supervised learning tasks. Further experimental studies prove the
effectiveness of different terms of MEGA.
