Graph Neural Networks (GNNs) have achieved impressive performance in many tasks on graph data. Recent studies show that they are vulnerable to adversarial attacks. Deliberate and unnoticeable perturbations on topology structure could render them near-useless in applications. How to design effective methods to improve the robustness of GNNs is a crucial problem. To solve this problem, some works attempt to design more robust GNN models, while others attempt to remove perturbations from the poisoned graph. Different from the previous works, this paper proposes a general framework termed as GraphReshape to enhance the robustness of GNNs via directly correcting the shifted classification boundary of GNN models in the presence of adversarial attacks. GraphReshape consists of two modules: locating tractive nodes that could correct GNNs and reshaping local structure to improve their representations in the latent space. Extensive experiments on four real-world datasets show that GraphReshape achieves significant performance gain compared with state-of-the-art baselines against different adversarial attacks.