Towards Robust Graph Contrastive Learning

• URL: http://arxiv.org/abs/2102.13085v1
• Date: Thu, 25 Feb 2021 18:55:15 GMT
• Title: Towards Robust Graph Contrastive Learning
• Authors: Nikola Jovanovi\'c, Zhao Meng, Lukas Faber, Roger Wattenhofer
• Abstract summary: We introduce a new method that increases the adversarial robustness of the learned representations. We evaluate the learned representations in a preliminary set of experiments, obtaining promising results.
• Score: 7.193373053157517
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study the problem of adversarially robust self-supervised learning on graphs. In the contrastive learning framework, we introduce a new method that increases the adversarial robustness of the learned representations through i) adversarial transformations and ii) transformations that not only remove but also insert edges. We evaluate the learned representations in a preliminary set of experiments, obtaining promising results. We believe this work takes an important step towards incorporating robustness as a viable auxiliary task in graph contrastive learning.

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