The Role of Graph Topology in the Performance of Biomedical Knowledge Graph Completion Models

• URL: http://arxiv.org/abs/2409.04103v1
• Date: Fri, 6 Sep 2024 08:09:15 GMT
• Title: The Role of Graph Topology in the Performance of Biomedical Knowledge Graph Completion Models
• Authors: Alberto Cattaneo, Stephen Bonner, Thomas Martynec, Carlo Luschi, Ian P Barrett, Daniel Justus,
• Abstract summary: We conduct a comprehensive investigation into the properties of publicly available biomedical Knowledge Graphs. We establish links to the accuracy observed in real-world applications. We release all model predictions and a new suite of analysis tools.
• Score: 3.1666540219908272
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Knowledge Graph Completion has been increasingly adopted as a useful method for several tasks in biomedical research, like drug repurposing or drug-target identification. To that end, a variety of datasets and Knowledge Graph Embedding models has been proposed over the years. However, little is known about the properties that render a dataset useful for a given task and, even though theoretical properties of Knowledge Graph Embedding models are well understood, their practical utility in this field remains controversial. 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 applications. 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.

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