Exploring Academic Relationships with UMAP: Dimensionality Reduction and Visualization of Topics and Authors in OpenAlex

Abstract

This thesis applies Uniform Manifold Approximation and Projection (UMAP) to analyse and visualize research works from the OpenAlex database. By using various embedding methods (including transformer-based models and hierarchical topic encodings) the study demonstrates that UMAP projections can effectively capture meaningful structures in the data, revealing relationships among research areas and institutions. Results show that capturing complex topic relationships across multiple domains is a challenging task. Nevertheless, the visualizations reveal significant thematic clusters and author groupings that align with our data analysis. Quan- titative evaluation using clustering metrics, such as the silhouette score, confirms the agreement between visual patterns and semantic embeddings. We also show the impact of UMAP hyperparameters on balancing local and global data structure preservation, which influences visualization clarity and interpretability. The resulting interactive, zoomable visual maps provide researchers with a powerful tool to explore and understand the organization of scientific knowledge.