Noisy Node

Noisy node classification in graph neural networks (GNNs) addresses the challenge of accurately classifying nodes in graphs where node labels are unreliable or contain errors. Current research focuses on developing robust GNN models that can mitigate the impact of noisy labels, employing techniques like multi-teacher distillation, class-wise node selection, and Bayesian label transition models integrated with label propagation. These advancements aim to improve the accuracy and reliability of GNN-based node classification in real-world applications where imperfect or incomplete data is common, impacting fields such as social network analysis and recommendation systems.