Abstract Graph Neural Networks (GNNs) are powerful in learning rich network
representations that aid the performance of downstream tasks. However, recent studies …

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

Graph neural networks (GNNs) have achieved remarkable success in various real-world
applications. However, recent studies highlight the vulnerability of GNNs to malicious …

Graph neural network (GNN) has achieved remarkable success in various graph learning
tasks, such as node classification, link prediction, and graph classification. The key to the …

Graph Neural Networks (GNNs) excel across various applications but remain vulnerable to
adversarial attacks, particularly Graph Injection Attacks (GIAs), which inject malicious nodes …

To increase security for devices connected to the internet, research has gone into using
Graph Neural Networks (GNNs) to inhibit the spread of malware through detection. GNN …

Dynamic graph neural networks (DGNNs) have demonstrated their extraordinary value in
many practical applications. Nevertheless, the vulnerability of DNNs is a serious hidden …

Adversarial attacks on Graph Neural Networks (GNNs) have become a significant security
concern. Graph Injection Attack (GIA) enables an attacker to perturb GNN models by …