Graph neural networks (GNNs) have gained significant popularity as powerful deep learning
methods for processing graph data. However, centralized GNNs face challenges in data …

Graph Prompt Learning (GPL) bridges significant disparities between pretraining and
downstream applications to alleviate the knowledge transfer bottleneck in real-world graph …

Link prediction in dynamic graphs (LPDG) is an important research problem that has diverse
applications such as online recommendations, studies on disease contagion, organizational …

Backdoor attack is a powerful attack algorithm to deep learning model. Recently, GNN's
vulnerability to backdoor attack has been proved especially on graph classification task. In …

Neural network implementations are known to be vulnerable to physical attack vectors such
as fault injection attacks. As of now, these attacks were only utilized during the inference …

Backdoor attack injects malicious behavior to models such that inputs embedded with
triggers are misclassified to a target label desired by the attacker. However, natural features …

Graph convolutional networks (GCNs) have been very effective in addressing the issue of
various graph-structured related tasks, such as node classification and graph classification …

Graph classification is crucial in network analyses. Networks face potential security threats,
such as adversarial attacks. Some defense methods may trade off the algorithm complexity …

Machine learning models that use deep neural networks (DNNs) are vulnerable to backdoor
attacks. An adversary carrying out a backdoor attack embeds a predefined perturbation …

Dynamic link prediction (DLP) makes graph prediction based on historical information. Since
most DLP methods are highly dependent on the training data to achieve satisfying prediction …