TheMesocarp/koho

Full spectrum sheaf neural network over arbitrary CW complexes.

49
/ 100
Emerging

This project enables developers and researchers to build neural networks that can model complex data relationships across multi-dimensional structures, not just simple networks. It takes in topological data representations (like cell complexes) and outputs advanced diffusion models that understand connections beyond simple nodes, useful for those working with intricate data geometries. It is for machine learning researchers and advanced data scientists exploring novel neural network architectures.

Use this if you are a machine learning researcher or advanced data scientist needing to apply diffusion models to data structured as cellular complexes, where traditional graph neural networks are insufficient.

Not ideal if you are a practitioner looking for a ready-to-use tool for standard machine learning tasks, or if your data can be adequately modeled with simpler graph-based neural networks.

topological-data-analysis computational-topology geometric-deep-learning neural-network-research complex-systems-modeling
No Package No Dependents
Maintenance 10 / 25
Adoption 8 / 25
Maturity 16 / 25
Community 15 / 25

How are scores calculated?

Stars

16

Forks

4

Language

Rust

License

AGPL-3.0

Last pushed

Mar 01, 2026

Monthly downloads

4

Commits (30d)

0

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