Bootstrapping Informative Graph Augmentation via A Meta Learning Approach

• URL: http://arxiv.org/abs/2201.03812v1
• Date: Tue, 11 Jan 2022 07:15:13 GMT
• Title: Bootstrapping Informative Graph Augmentation via A Meta Learning Approach
• Authors: Hang Gao, Jiangmeng Li, Wenwen Qiang, Lingyu Si, Changwen Zheng, Fuchun Sun
• Abstract summary: In graph contrastive learning, benchmark methods apply various graph augmentation approaches. Most of the augmentation methods are non-learnable, which causes the issue of generating unbeneficial augmented graphs. We motivate our method to generate augmented graph by a learnable graph augmenter, called MEta Graph Augmentation (MEGA)
• Score: 21.814940639910358
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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