Deep learning tools are being used extensively in a range of scientific domains; in particular, there has been a steady increase in the number of geometric deep learning solutions proposed to a variety of problems involving structured or relational scientific data. In this work, we report on the performance of graph segmentation methods for two scientific datasets from different fields. Based on observations, we were able to discern the individual impact each type of graph segmentation methods has on the dataset and how they can be used as a precursors to deep learning pipelines.