The field of missing data imputation is witnessing significant advancements, particularly in the integration of sophisticated distance metrics and graph neural networks. Recent developments emphasize the importance of leveraging complex data structures, such as bipartite and complete directed graphs, to capture intricate interdependencies among features. This approach not only enhances the accuracy of imputation but also improves the model's ability to generalize to new data points. Additionally, the incorporation of temporal smoothing mechanisms in graph neural networks is proving effective for longitudinal data, enabling more robust and scalable imputation solutions. Notably, the modularity of deep learning pipelines is being explored to decouple imputation from downstream tasks, allowing for more objective assessments and flexible reuse of imputation models. These innovations collectively push the boundaries of what is possible in handling missing data, making substantial contributions to various applications, especially in healthcare and longitudinal studies.