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 demonstrated their powerful capability in learning
representations for graph-structured data. Consequently, they have enhanced the …

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) have gained widespread usage and achieved
remarkable success in various real-world applications. Nevertheless, recent studies reveal …

Graph neural networks (GNNs) have achieved tremendous success in the task of graph
classification and its diverse downstream real-world applications. Despite the huge success …

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

Graph deep learning models, such as graph convolutional networks (GCN) achieve
remarkable performance for tasks on graph data. Similar to other types of deep models …

Despite the recent success of graph convolution networks (GCNs) in modeling graph
structured data, its vulnerability to adversarial attacks have been revealed and attacks on …

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

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 …