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

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

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 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 …

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 …

Recent work has shown that graph neural networks (GNNs) are vulnerable to adversarial
attacks on graph data. Common attack approaches are typically informed, ie they have …

Graph-structured data exist in numerous applications in real life. As a state-of-the-art graph
neural network, the graph convolutional network (GCN) plays an important role in …

Deep learning on graph structures has shown exciting results in various applications.
However, few attentions have been paid to the robustness of such models, in contrast to …