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cs.LG2026
Rethinking Message Passing as Retrieval for Text-Attributed Graph Learning
Jintang Li, Yuhong Chen, Ruofan Wu +4
Graph neural networks (GNNs) are typically conceptualized as message-passing neural networks, yet it remains unclear why neighborhood aggregation reliably outperforms node-wise mul…
cs.LG2026
Grad2Fair: A Gradient-driven Approach for Graph Fairness without Demographics
Yuchang Zhu, Zezhong Xie, Huizhe Zhang +4
Graph neural networks (GNNs) frequently encounter group fairness issues, often yielding biased predictions against specific demographic groups defined by sensitive attributes such…