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 …

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

Graph convolutional networks (GCNs) have emerged as one of the most popular neural
networks for a variety of tasks over graphs. Despite their remarkable learning and inference …

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 …

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

Despite achieving superior performance for many graph-related tasks, recent works have
shown that Graph Neural Networks (GNNs) are vulnerable to adversarial attacks on graph …

Graph Neural Networks (GNNs) have boosted the performance of many graph related tasks
such as node classification and graph classification. Recent researches show that graph …

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