# The role of graph topology in the performance of biomedical Knowledge Graph Completion models

## TL;DR

This paper explores how the structure of biomedical knowledge graphs affects the performance of models used to complete missing information in these graphs.

## Contribution

The study introduces a new analysis framework linking graph topology to model accuracy in biomedical knowledge graph completion.

## Key findings

- The topological properties of biomedical knowledge graphs significantly influence model performance.
- Publicly available datasets and models show varying effectiveness depending on graph structure.
- The release of analysis tools encourages further research into biomedical knowledge graph modeling.

## Abstract

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.

The code used to perform experiments and analyze results in this article as well as all experimental data is available at https://github.com/graphcore-research/kg-topology-toolbox/tree/main/the_role_of_graph_topology_paper and archived on Zenodo, at https://doi.org/10.5281/zenodo.12097376.