DeFence: Robust Node Classification under Joint Label-Structure Noise

How can we accurately classify nodes in graphs when both labels and edges are corrupted? Node classification under joint label and structure corruption is important in applications such as fraud detection and citation analysis. For example, in citation networks, errors in automated paper tagging may assign incorrect labels to papers, while errors in reference extraction may introduce spurious links between them. However, most prior works tackle only one noise source, over-trusting structure for label noise and labels for structure noise; when both occur, these assumptions amplify each other’s errors. In this paper, we propose DeFence, a robust node classification model under joint label-structure noise. DeFence constructs anchors from pre-propagation embeddings, avoiding reliance on noisy edges and labels. These anchors regularize representation learning and predictions under joint noise, while the resulting embeddings are periodically reclustered to refine the anchors. This mutual refinement is realized through anchor-filtered contrastive learning, anchor-posterior alignment, and gradient-matched supervision. We theoretically show that, under a better-than-random anchor assumption, anchor filtering improves class discrimination. Experiments on standard benchmarks demonstrate consistent improvements over strong baselines.