  With the increasing popularity of graph-based learning, graph neural networks
(GNNs) emerge as the essential tool for gaining insights from graphs. However,
unlike the conventional CNNs that have been extensively explored and
exhaustively tested, people are still worrying about the GNNs' robustness under
the critical settings, such as financial services. The main reason is that
existing GNNs usually serve as a black-box in predicting and do not provide the
uncertainty on the predictions. On the other side, the recent advancement of
Bayesian deep learning on CNNs has demonstrated its success of quantifying and
explaining such uncertainties to fortify CNN models. Motivated by these
observations, we propose UAG, the first systematic solution to defend
adversarial attacks on GNNs through identifying and exploiting hierarchical
uncertainties in GNNs. UAG develops a Bayesian Uncertainty Technique (BUT) to
explicitly capture uncertainties in GNNs and further employs an
Uncertainty-aware Attention Technique (UAT) to defend adversarial attacks on
GNNs. Intensive experiments show that our proposed defense approach outperforms
the state-of-the-art solutions by a significant margin.
