Fortifying Robustness in Graph Neural Networks: A Loss Correction Approach to Mitigate Label Noise

Handling noisy labels during neural network training is fundamental, as such noise can skew model optimization, leading to diminished performance. This article zeroes in on node classification using graph neural networks (GNNs), renowned for their adeptness in harnessing both node attributes and edge connections. Nevertheless, GNNs, due to their inherent feature propagation and information amalgamation, are more susceptible to misdirection from label noise than conventional neural networks. While existing robust GNNs offer defenses against noise's adverse effects, they often underrate risks posed by noisy labels during training or undervalue the significance of fortifying edges against erroneous aggregation and noisy edges. Addressing these gaps, we introduce a novel robust GNN framework, noise-mitigating GNN (NomiGNN), designed to enhance the robustness of GNNs against label noise. NomiGNN estimates noise distributions and refines loss optimization to counteract noise during training. Moreover, by instituting edge labels for a fresh prediction task, we facilitate learning sample relationships via same-label probabilities, mitigating mis-aggregation from noisy edges and bolstering node classification. Supplementing this, we incorporate pseudoedge labeling and iterative learning to remedy label shortages and estimation inaccuracies. NomiGNN is substantiated by both theoretical rationale and empirical evidence. Experimental evaluations on five real-world graphs highlight NomiGNN's superior resilience against noisy label corruption, outperforming eight benchmark GNN models.