  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.
