Graph neural networks (GNNs) have been increasingly deployed in various applications that
involve learning on non-Euclidean data. However, recent studies show that GNNs are …

Recent studies have shown that graph neural networks (GNNs) are vulnerable to
unnoticeable adversarial perturbations, which largely confines their deployment in many …

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 network (GNN), as a powerful representation learning model on graph data,
attracts much attention across various disciplines. However, recent studies show that GNN is …

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

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

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 …

Deep learning methods for graphs achieve remarkable performance on many tasks.
However, despite the proliferation of such methods and their success, recent findings …

Graph neural networks (GNNs) achieve remarkable performance for tasks on graph data.
However, recent works show they are extremely vulnerable to adversarial structural …