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cs.LG2026
Learning Dynamic Graph Representations through Timespan View Contrasts
Yiming Xu, Zhen Peng, Bin Shi +2
The rich information underlying graphs has inspired further investigation of unsupervised graph representation. Existing studies mainly depend on node features and topological prop…
cs.LG2026
Generalist Graph Anomaly Detection via Prototype-Based Distillation
Yiming Xu, Zihan Chen, Zhen Peng +4
Driven by the pressing demand for graph anomaly detection (GAD) in high-stakes domains, the generalist GAD paradigm, which trains a single detector transferable across new graphs,…
cs.LG2026
Hide and Find: A Distributed Adversarial Attack on Federated Graph Learning
Jinshan Liu, Ken Li, Jiazhe Wei +2
Federated Graph Learning (FedGL) is vulnerable to malicious attacks, yet developing a truly effective and stealthy attack method remains a significant challenge. Existing attack me…