Data augmentation has been widely used across various research domains in recent years. However, data augmentation applied to real-world graph-structured data tends to suffer from weak information, such as missing structural elements, incomplete features, and limited label availability. Graphs with weak information lack adequate training data to guide the graph learning process, resulting in performance loss. This article presents a novel automated data augmentation for graphs with weak information, namely AutoDAW, which aims to derive more informative node representations from incomplete input graphs in an adaptive way. To learn local features at the subgraph level, we adopt a reinforcement learning method to optimize the aggregation range for each node. To capture global semantic information, we present a graph diffusion-based long-range propagation method to facilitate more effective node feature propagation and aggregation. The dual-stage information aggregation promotes synergistic interactions between the two node representations. Extensive experiments on real-world datasets demonstrate the effectiveness, generalization, and robustness of AutoDAW.