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, most methods still optimize directly on training nodes using their possibly corrupted labels as supervision signals. In this work, we propose a prototype-guided framework that replaces direct label supervision over training nodes with semantic supervision derived from class-level prototypes. Each prototype is formed by aggregating representations of nodes sharing the same observed label and serves as a semantic anchor for guiding the classifier. To address the inherent supervision sparsity introduced by limited prototype instances, we introduce a dual-branch mixup strategy that integrates prototypes with high-confidence nodes through intra- and inter-class interpolation, which enhances supervision coverage and improves representation continuity. We further constrain the spatial variance of these samples to promote intra-class compactness. Theoretically, we demonstrate that the constructed prototypes remain aligned with true class semantics under bounded noise rates. Experiments on node classification tasks confirm the effectiveness of our approach under label noise and limited supervision.