The Role of Graph Topology in the Performance of Biomedical Knowledge Graph Completion Models
<|reference_start|>The Role of Graph Topology in the Performance of Biomedical Knowledge Graph Completion Models: 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.<|reference_end|>
@article{cattaneo2024the,
    title={The Role of Graph Topology in the Performance of Biomedical Knowledge
  Graph Completion Models},
    author={Alberto Cattaneo, Stephen Bonner, Thomas Martynec, Carlo Luschi, Ian P
  Barrett, Daniel Justus},
    journal={arXiv preprint arXiv:2409.04103},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.04103},
    primaryClass={cs.LG cs.AI q-bio.QM}
}