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 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 …

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 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) which apply the deep neural networks to graph data have
achieved significant performance for the task of semi-supervised node classification …

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

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 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 promising results in various tasks such as
node classification and graph classification. Recent studies find that GNNs are vulnerable to …