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arXiv · 2102.13085

Towards Robust Graph Contrastive Learning

Nikola Jovanović·Zhao Meng·Lukas Faber·Roger Wattenhofer
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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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BibTeXRIS

Nikola Jovanović, Zhao Meng, Lukas Faber, Roger Wattenhofer. 2021-02-25. Towards Robust Graph Contrastive Learning. https://arxiv.org/abs/2102.13085

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