Abstract Graph Neural Networks (GNNs) have gained widespread usage and achieved
remarkable success in various real-world applications. Nevertheless, recent studies reveal …

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 …

With recent advancements, graph neural networks (GNNs) have shown considerable
potential for various graph-related tasks, and their applications have gained considerable …

Graph neural network (GNN), as a powerful representation learning model on graph data,
attracts much attention across various disciplines. However, recent studies show that GNN is …

Graph neural networks (GNNs) have achieved tremendous success, but recent studies have
shown that GNNs are vulnerable to adversarial attacks, which significantly hinders their use …

Abstract Graph Neural Networks (GNNs) are powerful tools in graph application areas.
However, recent studies indicate that GNNs are vulnerable to adversarial attacks, which can …

Graph neural networks (GNNs) have achieved great success on various graph tasks.
However, recent studies have re-vealed that GNNs are vulnerable to adversarial attacks …

Deep learning-based models have demonstrated exceptional performances in diverse
fields. However, recent research has revealed that adversarial attacks and minor input …

Graph convolutional networks (GCNs) have been shown to be vulnerable to small
adversarial perturbations, which becomes a severe threat and largely limits their …

Graph Neural Networks (GNNs) have demonstrated their powerful capability in learning
representations for graph-structured data. Consequently, they have enhanced the …