The self-supervised graph reconstruction approach used in SMART may have limitations in scenarios where the graph undergoes rapid and significant structural changes. In such cases, the reconstruction process may struggle to capture the evolving graph dynamics accurately, leading to information loss and reduced generalization performance. To address this limitation, the reconstruction approach can be further improved by incorporating more advanced graph generation models that can adapt to dynamic changes in the graph structure. Techniques such as graph generative adversarial networks (GANs) or graph autoencoders can be explored to enhance the reconstruction process and improve the fidelity of the reconstructed graphs. Additionally, leveraging reinforcement learning algorithms to optimize the reconstruction process based on feedback from the generalization estimation performance can also enhance the effectiveness of the self-supervised graph reconstruction approach in SMART.