Recent studies demonstrate that Graph Neural Networks (GNNs) are vulnerable to slight but
adversarially designed perturbations, known as adversarial examples. To address this …

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

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

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

Recent studies have shown that graph neural networks (GNNs) are vulnerable to
unnoticeable adversarial perturbations, which largely confines their deployment in many …

Despite its success in the image domain, adversarial training did not (yet) stand out as an
effective defense for Graph Neural Networks (GNNs) against graph structure perturbations …

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

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 …

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 …