2026
Prototype-Guided Supervision for Graph Learning with Noisy and Sparse Labels
AAAI 2026technical
Graph learning faces major challenges under noisy and sparse supervision, where corrupted labels mislead representation learning and impair generalization. Prior work proposes robust training strategies such as correction, reweighting, and denoising to reduce the influence of noisy labels. However,