Knowledge Graph Completion has been increasingly adopted as a useful method for helping address several tasks in biomedical research, such as drug repurposing or drug-target identification. To that end, a variety of datasets and Knowledge Graph Embedding models have been proposed over the years. However, little is known about the properties that render a dataset, and associated modelling choices, useful for a given task. Moreover, even though theoretical properties of Knowledge Graph Embedding models are well understood, their practical utility in this field remains controversial. In this work, we conduct a comprehensive investigation into the topological properties of publicly available biomedical Knowledge Graphs and establish links to the accuracy observed in real-world tasks. By releasing all model predictions and a new suite of analysis tools we invite the community to build upon our work and continue improving the understanding of these crucial applications.
@article{arxiv.2409.04103,
title = {The Role of Graph Topology in the Performance of Biomedical Knowledge Graph Completion Models},
author = {Alberto Cattaneo and Stephen Bonner and Thomas Martynec and Edward Morrissey and Carlo Luschi and Ian P Barrett and Daniel Justus},
journal= {arXiv preprint arXiv:2409.04103},
year = {2025}
}