Fortifying Robustness in Graph Neural Networks: A Loss Correction Approach to Mitigate Label Noise
Summary
This article addresses the problem of label noise (incorrect training data labels) in graph neural networks (GNNs, which are AI models that learn from networks of connected data points). GNNs are especially vulnerable to label noise because they share information across connected nodes, which can amplify mistakes. The authors propose NomiGNN, a new framework that estimates noise patterns in the training data and adjusts the learning process to reduce noise's negative effects.
Solution / Mitigation
The source describes NomiGNN's approach: it 'estimates noise distributions and refines loss optimization to counteract noise during training,' incorporates 'edge labels for a fresh prediction task' to learn relationships between samples, includes 'pseudoedge labeling and iterative learning to remedy label shortages and estimation inaccuracies.' The authors demonstrate NomiGNN's effectiveness through experiments on real-world graphs, showing it 'outperform[s] eight benchmark GNN models' in robustness against noisy labels.
Classification
Original source: http://ieeexplore.ieee.org/document/11428305
First tracked: September 19, 2026 at 02:03 AM
Classified by LLM (prompt v3) · confidence: 85%