Abstract Graph Contrastive Learning (GCL) has attracted considerable interest due to its
impressive node representation learning capability. Despite the wide application of GCL …

Graph neural networks (GNNs) have shown significant performance in various practical
applications due to their strong learning capabilities. Backdoor attacks are a type of attack …

Graph neural networks (GNNs) have emerged as a powerful model to capture critical graph
patterns. Instead of treating them as black boxes in an end-to-end fashion, attempts are …

Abstract In recent years, Graph Neural Networks (GNNs) have achieved excellent
applications in classification or prediction tasks. Recent studies have demonstrated that …

Privacy concerns on social networks have received extensive attention in recent years. The
task of user identity linkage (UIL), which aims to identify corresponding users across different …

We study deceptive fairness attacks on graphs to answer the following question: How can
we achieve poisoning attacks on a graph learning model to exacerbate the bias …

Graph augmentation has received great attention in recent years for graph contrastive
learning (GCL) to learn well-generalized node/graph representations. However, mainstream …

Graphs neural networks (GNNs) have emerged as a powerful graph learning model due to
their superior capacity in capturing critical graph patterns. To gain insights about the model …

This paper introduces adversarial attacks targeting a Graph Neural Network (GNN)-based
radio resource management system in point-to-point (P2P) communications. Our focus lies …

Recent studies have shown that Graph Neural Networks (GNNs) are vulnerable to well-
designed and imperceptible adversarial attack. Attacks utilizing gradient information are …