Abstract
In many data-driven applications, higher-order relationships among multiple objects are essential to capture complex interactions. Hypergraphs, which generalize graphs by allowing edges to connect any number of nodes, provide a flexible and powerful framework for modeling such higher-order relationships. In this work, we introduce hypergraph diffusion wavelets and describe their favorable spectral and spatial properties. We demonstrate their utility for biomedical discovery in spatially resolved transcriptomics by applying the method to represent disease-relevant cellular niches for Alzheimer’s disease.
| Original language | English |
|---|---|
| Journal | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
| DOIs | |
| State | Published - 2025 |
| Event | 2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025 - Hyderabad, India Duration: 6 Apr 2025 → 11 Apr 2025 |
Keywords
- Alzheimer’s disease
- hyperedge
- hypergraph
- representation learning
- spatial transcriptomics
- wavelets
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