Jiansen Song

PAGPL: Privacy-Aware Graph Prompt Learning Scheme via Adaptive Perturbation-Estimated Topology Recovery

Graph prompt learning (GPL) serves as a crucial framework for mitigating the knowledge transfer by reconciling the substantial mismatch between pre-training models and downstream tasks. However, prevalent GPL paradigm fail to accommodate graph data affected by privacy-induced noise. Specifically, 1)