Despite the recent advances of graph neural networks (GNNs) in modeling graph data, the
training of GNNs on large datasets is notoriously hard due to the overfitting. Adversarial …

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

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 …

ABSTRACT Graph Neural Networks (GNNs) have achieved state-of-the-art results on a
variety of graph learning tasks, however, it has been demonstrated that they are vulnerable …

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 …

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 …

Graph Neural Networks (GNNs) have achieved impressive performance in many tasks on
graph data. Recent studies show that they are vulnerable to adversarial attacks. Deliberate …

Graph Neural Networks (GNNs) are powerful tools in representation learning for graphs.
However, recent studies show that GNNs are vulnerable to carefully-crafted perturbations …

Graph Neural Networks (GNNs) have emerged as the dominant approach for machine
learning on graph-structured data. However, concerns have arisen regarding the …

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 …