Black-box adversarial attacks have demonstrated strong potential to compromise machine
learning models by iteratively querying the target model or leveraging transferability from a …

Real-world graphs such as social networks, communication networks, and rating networks
are constantly evolving over time. Many deep learning architectures have been developed …

With recent advancements, graph neural networks (GNNs) have shown considerable
potential for various graph-related tasks, and their applications have gained considerable …

Graph neural network (GNN), the mainstream method to learn on graph data, is vulnerable
to graph evasion attacks, where an attacker slightly perturbing the graph structure can fool …

Graph-structured data exist in numerous applications in real life. As a state-of-the-art graph
neural network, the graph convolutional network (GCN) plays an important role in …

Research and analysis of attacks on dynamic graph is beneficial for information systems to
investigate vulnerabilities and strength abilities in resisting malicious attacks. Existing …

Graph learning methods have achieved remarkable performance in various domains such
as social recommendation, financial fraud detection, and so on. In real applications, the …

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

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

Real-world graphs such as social networks, communication networks, and rating networks
are constantly evolving over time. Many architectures have been de, veloped to learn …