  Recent studies have revealed that GNNs are vulnerable to adversarial attacks.
Most existing robust graph learning methods measure model robustness based on
label information, rendering them infeasible when label information is not
available. A straightforward direction is to employ the widely used Infomax
technique from typical Unsupervised Graph Representation Learning (UGRL) to
learn robust unsupervised representations. Nonetheless, directly transplanting
the Infomax technique from typical UGRL to robust UGRL may involve a biased
assumption. In light of the limitation of Infomax, we propose a novel unbiased
robust UGRL method called Robust Graph Information Bottleneck (RGIB), which is
grounded in the Information Bottleneck (IB) principle. Our RGIB attempts to
learn robust node representations against adversarial perturbations by
preserving the original information in the benign graph while eliminating the
adversarial information in the adversarial graph. There are mainly two
challenges to optimize RGIB: 1) high complexity of adversarial attack to
perturb node features and graph structure jointly in the training procedure; 2)
mutual information estimation upon adversarially attacked graphs. To tackle
these problems, we further propose an efficient adversarial training strategy
with only feature perturbations and an effective mutual information estimator
with subgraph-level summary. Moreover, we theoretically establish a connection
between our proposed RGIB and the robustness of downstream classifiers,
revealing that RGIB can provide a lower bound on the adversarial risk of
downstream classifiers. Extensive experiments over several benchmarks and
downstream tasks demonstrate the effectiveness and superiority of our proposed
method.
