Despite achieving strong performance in semi-supervised node classification task, graph
neural networks (GNNs) are vulnerable to adversarial attacks, similar to other deep learning …

Despite achieving great success, graph neural networks (GNNs) are vulnerable to
adversarial attacks. Existing defenses focus on developing adversarial training or model …

In this paper, we study the robustness of graph convolutional networks (GCNs). Despite the
good performance of GCNs on graph semi-supervised learning tasks, previous works have …

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

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

Graph Convolutional Networks (GCNs) are an emerging type of neural network model on
graphs which have achieved state-of-the-art performance in the task of node classification …

Graph Neural Networks have demonstrated exceptional performance in a variety of graph
learning tasks, but their vulnerability to adversarial attacks remains a major concern …

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) has achieved tremendous development on perceptual tasks
in recent years, such as node classification, graph classification, link prediction, etc …