This work addresses the challenge of data augmentation in graph neural networks (GNNs), where the intricate structure and interdependencies of graph data limit the applicability of conventional augmentation techniques. While prior approaches focus on perturbing the input graph (such as masking features or dropping nodes and edges) we explore augmentation in the latent space. We propose a novel architecture that incorporates a data augmentation module with integrated denoising and structural refinement capabilities. Leveraging supervised signals, our framework guides the GNN toward learning enhanced latent representations with reduced noise and irrelevant connections. Extensive experiments on diverse graph datasets demonstrate that our method consistently outperforms standard GNN architectures in node classification tasks.