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HyHG: A Temporal Hypergraph Contrastive Learning Framework for Biomedical Hypothesis Generation
Conference proceeding

HyHG: A Temporal Hypergraph Contrastive Learning Framework for Biomedical Hypothesis Generation

Amir Hassan Shariatmadari, Sikun Guo, Nathan C. Sheffield, Aidong Zhang and Kishlay Jha
Proceedings (IEEE International Conference on Data Mining), pp.703-712
11/12/2025
DOI: 10.1109/ICDM65498.2025.00078

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Abstract

Biomedical research now generates more than a million articles annually, overwhelming researchers and hindering discovery. This surge has sparked interest in biomedical hypothesis generation (HG), which aims to uncover implicit patterns among biomedical concepts. Most existing methods focus on pairwise link prediction, overlooking the complex, multi-concept relationships underlying many breakthroughs. We introduce HyHG, a temporal Hypergraph contrastive learning framework for biomedical Hypothesis Generation, which redefines hypotheses as hyperedges-sets of co-mentioned concepts in an article. By representing articles as hyperedges and organizing them into a temporal hypergraph, HyHG captures the evolution of scientific ideas over time. A transformer-based architecture learns from historical hyperedge sequences to predict future hyperedges-sets of concepts likely to co-occur in future literature. To distinguish genuine hypotheses from misleading ones, HyHG employs a timeanchored contrastive loss and hard negative sampling based on minimal edits to real hyperedges. We demonstrate state-of-the-art performance on three biomedical datasets. Our code and data are available at: https://github.com/amirhassan25/Temporal-Hypergraph-Contrastive-Learning.
 Data Mining Biological system modeling Codes Contrastive learning Hypergraphs Hypothesis Generation Surges Temporal Graph Learning Transformers

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