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InfoResearchPeer-reviewed
Fortifying Robustness in Graph Neural Networks: A Loss Correction Approach to Mitigate Label Noise
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Summary
The article presents NomiGNN, a robust graph neural network framework for node classification that defends against label noise. It estimates noise distributions and refines loss optimization during training, and adds an edge-label prediction task using same-label probabilities to limit mis-aggregation from noisy edges. Experiments on five real-world graphs show it outperforms eight benchmark GNN models.