Graph neural networks (GNNs) are vulnerable to adversarial perturbations, including those
that affect both node features and graph topology. This paper investigates GNNs derived …

Benefiting from the message passing mechanism, Graph Neural Networks (GNNs) have
been successful on flourish tasks over graph data. However, recent studies have shown that …

Node injection attack on Graph Neural Networks (GNNs) is an emerging and practical attack
scenario that the attacker injects malicious nodes rather than modifying original nodes or …

Recommendation systems (RS) are crucial for alleviating the information overload problem.
Due to its pivotal role in guiding users to make decisions, unscrupulous parties are lured to …

Abstract Graph Neural Networks (GNNs) have demonstrated remarkable success across
diverse fields, yet remain susceptible to subtle adversarial perturbations that significantly …

Graph neural networks (GNNs) have achieved impressive performance in various graph-
related tasks. However, recent studies have found that GNNs are vulnerable to adversarial …

Real-time traffic prediction models play a pivotal role in smart mobility systems and have
been widely used in route guidance, emerging mobility services, and advanced traffic …

近年来, 图神经网络(GNNs) 逐渐成为人工智能的重要研究方向. 然而, GNNs
的对抗脆弱性使其实际应用面临严峻挑战. 为了全面认识GNNs 对抗攻击与鲁棒性评测的研究 …

Graph perturbation hinders graph models in real applications, and thus defense methods
against graph perturbation have been attracting increasing attention. However, current …

Abstract Graph Neural Networks (GNNs) have gained widespread usage and achieved
remarkable success in various real-world applications. Nevertheless, recent studies reveal …