Graph neural networks (GNNs) are widely applied in real-life scenarios due to their excellent
performance in processing graph data. Meanwhile, GNNs are vulnerable to the node …

Graph Neural Networks (GNNs) are powerful but vulnerable to adversarial attacks,
necessitating the research on certified robustness that can provide GNNs with robustness …

We investigate certified robustness for GNNs under graph injection attacks. Existing
research only provides sample-wise certificates by verifying each node independently …

Graph neural networks (GNNs) achieve the state-of-the-art on graph-relevant tasks such as
node and graph classification. However, recent works show GNNs are vulnerable to …

Graph Neural Networks (GNNs) are powerful but vulnerable to adversarial attacks,
necessitating the research on certified robustness that can provide GNNs with robustness …