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Automated Data Augmentations for Graph Classification [article]

Youzhi Luo, Michael McThrow, Wing Yee Au, Tao Komikado, Kanji Uchino, Koji Maruhashi, Shuiwang Ji
<span title="2022-04-18">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Data augmentations are effective in improving the invariance of learning machines. We argue that the corechallenge of data augmentations lies in designing data transformations that preserve labels. This is relativelystraightforward for images, but much more challenging for graphs. In this work, we propose GraphAug, a novelautomated data augmentation method aiming at computing label-invariant augmentations for graph classification.Instead of using uniform transformations as in existing studies,
more &raquo; ... raphAug uses an automated augmentationmodel to avoid compromising critical label-related information of the graph, thereby producing label-invariantaugmentations at most times. To ensure label-invariance, we develop a training method based on reinforcementlearning to maximize an estimated label-invariance probability. Comprehensive experiments show that GraphAugoutperforms previous graph augmentation methods on various graph classification tasks.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2202.13248v3">arXiv:2202.13248v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wkiqkpjhjnbihdc7mkoxkrxt4a">fatcat:wkiqkpjhjnbihdc7mkoxkrxt4a</a> </span>
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