Uncertainty-aware Attention Graph Neural Network for Defending Adversarial Attacks

Understand

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.