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

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 been widely applied to different tasks such as
bioinformatics, drug design, and social networks. However, recent studies have shown that …

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

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) enable many novel applications and achieve excellent
performance. However, their performance may be significantly degraded by the graph …

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) have achieved state-of-the-art performance in various graph
structure related tasks such as node classification and graph classification. However, GNNs …

Deep learning models for graphs have achieved strong performance for the task of node
classification. Despite their proliferation, little is known about their robustness to adversarial …

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 …