https://github.com/expectozjj/generalisedformanricci

Forman Ricci Curvature for Simplicial Complex.
https://github.com/expectozjj/generalisedformanricci

differential-geometry forman graph-network networkx ricci-curvature simplicial-complex

Last synced: 4 months ago
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Forman Ricci Curvature for Simplicial Complex.

• Host: GitHub
• URL: https://github.com/expectozjj/generalisedformanricci
• Owner: ExpectozJJ
• License: apache-2.0
• Created: 2020-05-06T15:59:09.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2022-09-22T09:27:24.000Z (over 3 years ago)
• Last Synced: 2025-10-21T19:46:58.424Z (8 months ago)
• Topics: differential-geometry, forman, graph-network, networkx, ricci-curvature, simplicial-complex
• Language: Jupyter Notebook
• Homepage:
• Size: 1.82 MB
• Stars: 9
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
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# GeneralisedFormanRicci

This code computes the Forman Ricci Curvature for simplicial complex generated from a given point cloud data. The implementation is based on the combinatorial definition of Forman Ricci curvature defined by Robin Forman. This implementation generalises beyond the simplified version implemented in saibalmars/GraphRicciCurvature github.

Many thanks to stephenhky and saibalmars for their packages MoguTDA and GraphRicciCurvature respectively. 

Partial code was modified from MoguTDA for the computation of the boundary matrices. 

## Installation via conda-forge

[![Anaconda-Server Badge](https://img.shields.io/badge/install%20with%20-conda--forge-blue)](https://anaconda.org/conda-forge/generalisedformanricci)

![Conda (channel only)](https://img.shields.io/conda/vn/conda-forge/generalisedformanricci)

![Conda](https://img.shields.io/conda/dn/conda-forge/generalisedformanricci?color=green)

![Conda](https://img.shields.io/conda/pn/conda-forge/generalisedformanricci?color=red)

Installing `generalisedformanricci` from the `conda-forge` channel can be achieved by adding `conda-forge` to your channels with:

```

conda config --add channels conda-forge

```

Once the `conda-forge` channel has been enabled, `generalisedformanricci` can be installed with:

```

conda install generalisedformanricci

```

It is possible to list all of the versions of `generalisedformanricci` available on your platform with:

```

conda search generalisedformanricci --channel conda-forge

```

Alternatively, `generalisedformanricci` can be installed just by `conda install -c conda-forge generalisedformanricci`.

## Installation via pip

![PyPI](https://img.shields.io/pypi/v/GeneralisedFormanRicci)

![PyPI - Downloads](https://img.shields.io/pypi/dw/GeneralisedFormanRicci)

`pip install GeneralisedFormanRicci`

Upgrading via `pip install --upgrade GeneralisedFormanRicci`

## Package Requirement

* [NetworkX](https://github.com/networkx/networkx) >= 2.0 (Based Graph library)

* [GUDHI](https://github.com/GUDHI) (Simplicial Complex Library)

* [NumPy](https://github.com/numpy/numpy)

* [SciPy](https://github.com/scipy/scipy)

## Simple Example

```

from GeneralisedFormanRicci.frc import GeneralisedFormanRicci

data = [[0.8, 2.6], [0.2, 1.0], [0.9, 0.5], [2.7, 1.8], [1.7, 0.5], [2.5, 2.5], [2.4, 1.0], [0.6, 0.9], [0.4, 2.2]]

for f in [0, 0.5, 1, 2, 3]:

    sc = GeneralisedFormanRicci(data, method = "rips", epsilon = f)

    sc.compute_forman()

    sc.compute_bochner()

```

## References

* MoguTDA: https://github.com/stephenhky/MoguTDA

* GraphRicciCurvature: https://github.com/saibalmars/GraphRicciCurvature

* Forman, R. (2003). Bochner's method for cell complexes and combinatorial Ricci curvature. Discrete and Computational Geometry, 29(3), 323-374.

* Forman, R. (1999). Combinatorial Differential Topology and Geometry. New Perspectives in Algebraic Combinatorics, 38, 177.

## Cite 

If you use this code in your research, please considering cite our paper:

* JunJie Wee and Kelin Xia, Ollivier Persistent Ricci Curvature-Based Machine Learning for the Protein–Ligand Binding Affinity Prediction, Journal of Chemical Information and Modeling 2021 61 (4), 1617-1626

* JunJie Wee, Kelin Xia, Forman persistent Ricci curvature (FPRC)-based machine learning models for protein–ligand binding affinity prediction, Briefings in Bioinformatics, 2021;, bbab136

* D. Vijay Anand, Qiang Xu, JunJie Wee, Kelin Xia, Tze Chien Sum. Topological Feature Engineering for Machine Learning Based Halide Perovskite Materials Design. npj Computational Materials. 8, 203. (2022).