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.

中文翻译：

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迈向健壮的图对比学习

我们研究图上对抗性强的自我监督学习的问题。在对比学习框架中，我们引入了一种新方法，该方法通过i）对抗变换和ii）不仅去除边缘而且插入边缘的变换来提高学习表示的对抗鲁棒性。我们在一组初步的实验中评估了学习到的表示形式，获得了可喜的结果。我们认为这项工作迈出了重要的一步，将鲁棒性作为图对比学习中可行的辅助任务。