Graph Neural Networks (GNNs) have shown great performance in various tasks, with the core idea of gaining knowledge from labels and aggregating messages within the neighborhood. However, the prevailing challenges in most graphs are twofold: insufficient accurate (high-quality) labels and adequate neighbors for nodes, resulting in weak GNNs. Existing graph augmentation methods designed for these issues often tackle only one. Moreover, they either require extra training costs for generators, rely on overly simplistic strategies, or need substantial prior knowledge, all of which result in weak generalization abilities. To simultaneously address both of the two challenges faced by graphs in a generalized way, we propose an elegant method called IntraMix. IntraMix innovatively employs Mixup among low-quality (inaccurate) labeled data of the same class, generating high-quality labeled data at minimal cost. Additionally, it finds data with high confidence of being clustered into the same group as the generated data to serve as their neighbors, thereby enriching the neighborhoods of graphs. IntraMix efficiently tackles both issues faced by graphs and challenges the prior notion of the limited effectiveness of Mixup in node classification. IntraMix is a theoretically grounded plug-in-play framework that can be readily applied to all GNNs. Extensive experiments demonstrate the effectiveness of IntraMix across various GNNs and datasets.