Abstract Graph Contrastive Learning (GCL) has emerged as a popular unsupervised graph
representation learning method. However, it has been shown that GCL is vulnerable to …

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 …

Machine learning models are shown to face a severe threat from Model Extraction Attacks,
where a well-trained private model owned by a service provider can be stolen by an attacker …

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 state-of-the-art performance in many graph-
related tasks such as node classification. However, recent studies show that GNNs are …

Graph neural networks, a popular class of models effective in a wide range of graph-based
learning tasks, have been shown to be vulnerable to adversarial attacks. While the majority …

Influence function, a method from robust statistics, measures the changes of model
parameters or some functions about model parameters concerning the removal or …

Graph Convolutional Networks (GCNs) have fueled a surge of research interest due to their
encouraging performance on graph learning tasks, but they are also shown vulnerability to …

Explaining Graph Neural Network (XGNN) has gained growing attention to facilitate the trust
of using GNNs, which is the mainstream method to learn graph data. Despite their growing …

Graph Neural Networks (GNNs) have emerged as a critical tool for optimizing and managing
the complexities of the Internet of Things (IoT) in next-generation networks. This survey …