Towards Robust Graph Contrastive Learning

2021-02-25 · Nikola Jovanović, Zhao Meng, Lukas Faber, Roger Wattenhofer

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.

📄 PDF Abstract BibTeX arXiv:2102.13085

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Adversarial Robustness Contrastive Learning Self-Supervised Learning

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