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Manify is a Python library for generating graph/data embeddings and performing machine learning in product spaces with mixed curvature (hyperbolic, Euclidean, and spherical spaces). It provides tools for manifold creation, curvature estimation, embedding generation, and predictive modeling that respects the underlying geometry of complex data.
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You can read our manuscript here: [Manify: A Python Library for Learning Non-Euclidean Representations](https://arxiv.org/abs/2503.09576)
-`manify.utils.link_prediction` - Preprocessing graphs with link prediction
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-`manify.utils.visualization` - Tools for visualization
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## Research Background
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Manify implements geometric machine learning approaches described in academic papers, particularly focusing on handling data with mixed geometric properties. It's especially suited for data that naturally lives in non-Euclidean spaces, such as hierarchical data, networks, and certain types of biological data.
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## Archival branches
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This repo has a number of archival branches that contain code from previous versions of the library when it was under
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active development. These branches are not maintained and are provided for reference only:
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-[Dataset-Generation](https://github.com/pchlenski/manify/tree/Dataset-Generation). This branch contains code used to
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generate the datasets found in `manify.utils.dataloaders`.
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-[notebook-archive](https://github.com/pchlenski/manify/tree/notebook_archive). This branch contains dozens of Jupyter
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notebooks and datasets that were used to develop the library and carry out various benchmarks for the Mixed Curvature
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Decision Trees and Random Forests paper.
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## Citation
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## Contributing
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Please read our [contributing guide](https://github.com/pchlenski/manify/blob/main/CONTRIBUTING.md) for details on how
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to contribute to the project.
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## References
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If you use our work, please cite the `Manify` paper:
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```bibtex
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@misc{chlenski2025manifypythonlibrarylearning,
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url={https://arxiv.org/abs/2503.09576},
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}
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```
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Additionally, if you use one of the methods implemented in `manify`, please cite the original papers:
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-`CoordinateLearning`: Gu et al. "Learning Mixed-Curvature Representations in Product Spaces." ICLR 2019.
-`SiameseNetwork`: Based on Siamese networks: Chopra et al. "Learning a Similarity Metric Discriminatively, with Application to Face Verification." CVPR 2005. [https://ieeexplore.ieee.org/document/1467314](https://ieeexplore.ieee.org/document/1467314)
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-`ProductSpaceDT` and `ProductSpaceRF`: Chlenski et al. "Mixed Curvature Decision Trees and Random Forests." ICML 2025. [https://arxiv.org/abs/2410.13879](https://arxiv.org/abs/2410.13879)
-`ProductSpacePerceptron` and `ProductSpaceSVM`: Tabaghi et al. "Linear Classifiers in Product Space Forms." ArXiv 2021. [https://arxiv.org/abs/2102.10204](https://arxiv.org/abs/2102.10204)
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-`RiemannianFuzzyKMeans` and `RiemannianAdan`: Yuan et al. "Riemannian Fuzzy K-Means." OpenReview 2025. [https://openreview.net/forum?id=9VmOgMN4Ie](https://openreview.net/forum?id=9VmOgMN4Ie)
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- Delta-hyperbolicity computation: Based on Gromov's δ-hyperbolicity metric for tree-likeness of metric spaces. Gromov, M. "Hyperbolic Groups." Essays in Group Theory, 1987. [https://link.springer.com/chapter/10.1007/978-1-4613-9586-7_3](https://link.springer.com/chapter/10.1007/978-1-4613-9586-7_3)
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- Sectional curvature estimation: Gu et al. "Learning Mixed-Curvature Representations in Product Spaces." ICLR 2019. [https://openreview.net/forum?id=HJxeWnCcF7](https://openreview.net/forum?id=HJxeWnCcF7)
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- Greedy signature selection: Tabaghi et al. "Linear Classifiers in Product Space Forms." ArXiv 2021. [https://arxiv.org/abs/2102.10204](https://arxiv.org/abs/2102.10204)
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