Graph convolutional networks (GCNs) have emerged as one of the most popular neural
networks for a variety of tasks over graphs. Despite their remarkable learning and inference …

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 demonstrated their powerful capability in learning
representations for graph-structured data. Consequently, they have enhanced the …

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 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 achieved impressive performance in many tasks on
graph data. Recent studies show that they are vulnerable to adversarial attacks. Deliberate …

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) are a class of effective deep learning models for node
classification tasks; yet their predictive capability may be severely compromised under …

Graph Neural Networks (GNNs), such as Graph Convolutional Network, have exhibited
impressive performance on various real-world datasets. However, many researches have …

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 …