Author

Zhong Chen

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Conference Open access Sep 2026

Disentangled Graph-Enhanced Large Language Models for Fair Learning

Large Language Models (LLMs) achieve strong performance in many applications but remain limited in handling graph-structured data due to their reliance on textual context. Recent approaches integrate Graph Neural Networks (GNNs) to enhance structural modeling, yet they largely overlook fairness, leaving models vulnerab...

Zhi-Peng Yin, Zi-Chong Wang, Zhong Chen et al. · Proceedings of the Thirty-Fi... · 5 citations

Conference Open access Sep 2026

Learning Counterfactual Fairness from Authentic Generation

Graph Counterfactual Fairness (GCFair), a novel framework that achieves counterfactual fairness by explicitly identifying and disentangling the subsets of node features and graph structures genuinely affected by sensitive attributes, is proposed.

Zi-Chong Wang, Zhi-Peng Yin, Zhong Chen et al. · Proceedings of the Thirty-Fi... · 3 citations