NCPNET is a novel conformal prediction framework designed for temporal graph neural networks that addresses the limitations of existing methods focused on static graphs. By introducing a diffusion-based non-conformity score and an efficiency-aware optimization algorithm, NCPNET effectively captures temporal uncertainties and enhances computational efficiency, achieving significant improvements in prediction set sizes across various real-world temporal graph datasets.