Graph Neural Networks (GNNs) have boosted the performance of many graph related tasks
such as node classification and graph classification. Recent researches show that 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 tremendous success in many graph mining
tasks benefitting from the message passing strategy that fuses the local structure and node …

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

Graph Convolutional Networks (GCNs) are an emerging type of neural network model on
graphs which have achieved state-of-the-art performance in the task of node classification …

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 …

Recent studies have shown that Graph Convolutional Networks (GCNs) are vulnerable to
adversarial attacks on the graph structure. Although multiple works have been proposed to …

Recent studies have shown that graph neural networks (GNNs) are vulnerable against
perturbations due to lack of robustness and can therefore be easily fooled. Currently, most …

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 boosted the performance for many graph-related
tasks. Despite the great success, recent studies have shown that GNNs are still vulnerable to …