Learning Graph Augmentations to Learn Graph Representations

• URL: http://arxiv.org/abs/2201.09830v1
• Date: Mon, 24 Jan 2022 17:50:06 GMT
• Title: Learning Graph Augmentations to Learn Graph Representations
• Authors: Kaveh Hassani and Amir Hosein Khasahmadi
• Abstract summary: LG2AR is an end-to-end automatic graph augmentation framework. It helps encoders learn generalizable representations on both node and graph levels. It achieves state-of-the-art results on 18 out of 20 graph-level and node-level benchmarks.
• Score: 13.401746329218017
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Devising augmentations for graph contrastive learning is challenging due to their irregular structure, drastic distribution shifts, and nonequivalent feature spaces across datasets. We introduce LG2AR, Learning Graph Augmentations to Learn Graph Representations, which is an end-to-end automatic graph augmentation framework that helps encoders learn generalizable representations on both node and graph levels. LG2AR consists of a probabilistic policy that learns a distribution over augmentations and a set of probabilistic augmentation heads that learn distributions over augmentation parameters. We show that LG2AR achieves state-of-the-art results on 18 out of 20 graph-level and node-level benchmarks compared to previous unsupervised models under both linear and semi-supervised evaluation protocols. The source code will be released here: https://github.com/kavehhassani/lg2ar

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