Sheaf Neural Networks and biomedical applications
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Abstract (arXiv)
The purpose of this paper is to elucidate the theory and mathematical modelling behind the sheaf neural network (SNN) algorithm and then show how SNN can effectively answer to biomedical questions in a concrete case study and outperform the most popular graph neural networks (GNNs) as graph convolutional networks (GCNs), graph attention networks (GAT) and GraphSage.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 27 references that are in the map. First posted on arXiv 29 Jan 2026.
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9 of 27 references.
References · 9
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
- 2017Graph Attention Networkscited by 8,854
- 2017Inductive Representation Learning on Large Graphscited by 6,284
- 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
- 2022Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNscited by 208
- 2022Sheaf Neural Networks with Connection Laplacianscited by 64
- 2023Deep Learning and Geometric Deep Learning: an introduction for mathematicians and physicistscited by 3
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