Honghong Zeng

GD$^2$: Robust Graph Learning under Label Noise via Dual-View Prediction Discrepancy

Graph Neural Networks (GNNs) achieve strong performance in node classification tasks but exhibit substantial performance degradation under label noise. Despite recent advances in noise-robust learning, a principled approach that exploits the node-neighbor interdependencies inherent in graph data for…