3 papers
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…
cs.LG2025
Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks
Han Zhang, Yan Wang, Guanfeng Liu +3
To enhance the reliability and credibility of graph neural networks (GNNs) and improve the transparency of their decision logic, a new field of explainability of GNNs (XGNN) has em…
cs.LG2025
Adaptive Graph Unlearning
Pengfei Ding, Yan Wang, Guanfeng Liu +1
Graph unlearning, which deletes graph elements such as nodes and edges from trained graph neural networks (GNNs), is crucial for real-world applications where graph data may contai…