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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…
cs.IR2025
Causal-Invariant Cross-Domain Out-of-Distribution Recommendation
Jiajie Zhu, Yan Wang, Feng Zhu +3
Cross-Domain Recommendation (CDR) aims to leverage knowledge from a relatively data-richer source domain to address the data sparsity problem in a relatively data-sparser target do…