Before we consider GNN methods for learned representations , related forms of dimensionality reduction was explored. UMAP is an extension of the T-distributed Stochastic Neighbor Embedding (t-SNE) to visualize high-dimensional data. A looser clustering had to be applied with the use of unsupervised clustering techniques. The different models evaluated show different trends with regards to the number of features output by UMAP, as well as the nearest neighbors algorithm employed (Fig ).