Graph Neural Networks (GNNs) have emerged as the dominant approach for machine
learning on graph-structured data. However, concerns have arisen regarding the …

Graph neural networks (GNNs) have recently gained much attention for node and graph
classification tasks on graph-structured data. However, multiple recent works showed that an …

Graph Neural Networks (GNNs) are powerful tools in representation learning for graphs.
However, recent studies show that GNNs are vulnerable to carefully-crafted perturbations …

Despite the recent advances of graph neural networks (GNNs) in modeling graph data, the
training of GNNs on large datasets is notoriously hard due to the overfitting. Adversarial …

Despite the success of graph neural networks (GNNs), their vulnerability to adversarial
attacks poses tremendous challenges for practical applications. Existing defense methods …

Graph Neural Networks (GNNs) have achieved impressive performance in many tasks on
graph data. Recent studies show that they are vulnerable to adversarial attacks. Deliberate …

Graph neural networks (GNNs) achieve remarkable performances for the tasks on graph
data. However, recent studies uncover that they are extremely vulnerable to adversarial …

Graph neural networks (GNNs) have shown considerable promise for graph-structured data.
However, they are also known to be unstable and vulnerable to perturbations and attacks …

Graph neural networks (GNNs) have achieved significant success in graph representation
learning. Nevertheless, the recent work indicates that current GNNs are vulnerable to …

Deep learning methods for graphs achieve remarkable performance on many tasks.
However, despite the proliferation of such methods and their success, recent findings …