4 papers · 1 filter
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…
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…
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…
Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs
Pengfei Ding, Yan Wang, Guanfeng Liu +2
Heterogeneous graph few-shot learning (HGFL) has been developed to address the label sparsity issue in heterogeneous graphs (HGs), which consist of various types of nodes and edges…