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cs.LG2026
A Robust Watermark-based Fingerprint Framework for GNNs Ownership Verification
Han Zhang, Yan Wang, Guanfeng Liu +3
The high training cost of Graph Neural Networks (GNNs) has raised growing concerns regarding model ownership infringement, such as model stealing and unauthorized misuse. To verify…
cs.LG2026
Frequency-Corrupt Based Graph Self-Supervised Learning
Haojie Li, Mengjiao Zhang, Guanfeng Liu +3
Graph self-supervised learning can reduce the need for labeled graph data and has been widely used in recommendation, social networks, and other web applications. However, existing…
cs.LG2026
Re-understanding Graph Unlearning through Memorization
Pengfei Ding, Yan Wang, Guanfeng Liu
Graph unlearning (GU), which removes nodes, edges, or features from trained graph neural networks (GNNs), is crucial in Web applications where graph data may contain sensitive, mis…