With the great popularity of Graph Neural Networks (GNNs), the robustness of GNNs to
adversarial attacks has received increasing attention. However, existing works neglect …

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 …

Graph neural networks (GNNs) have achieved outstanding performance in semi-supervised
learning tasks with partially labeled graph structured data. However, labeling graph data for …

Graph Neural Networks (GNN) offer the powerful approach to node classification in complex
networks across many domains including social media, E-commerce, and FinTech …

Graph neural networks (GNNs) have achieved great success on various graph tasks.
However, recent studies have re-vealed that GNNs are vulnerable to adversarial attacks …

Graph neural networks (GNNs) have achieved state-of-the-art performance in many graph-
related tasks such as node classification. However, recent studies show that GNNs are …

Recent years have witnessed the emergence and development of graph neural networks
(GNNs), which have been shown as a powerful approach for graph representation learning …

Graph neural networks (GNNs) have been successfully used to analyze non-Euclidean
network data. Recently, there emerge a number of works to investigate the robustness of …

Graph Neural Networks (GNNs) have emerged as a series of effective learning methods for
graph-related tasks. However, GNNs are shown vulnerable to adversarial attacks, where …

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 …