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Bootstrapping Informative Graph Augmentation via A Meta Learning Approach. (arXiv:2201.03812v1 [cs.LG])
Jan. 12, 2022, 2:10 a.m. | Hang Gao, Jiangmeng Li, Wenwen Qiang, Lingyu Si, Changwen Zheng, Fuchun Sun
cs.LG updates on arXiv.org arxiv.org
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 …
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