PyGraphon

Welcome to the documentation of our Python library for estimating graphons from observed data! Our library provides a powerful set of tools for estimating graphons, which are a type of object used to model large, random graphs, from observed data.

from pygraphon import HistogramEstimator

#  sample a graph with n nodes from one of the built-in graphons f(x,y) = x*y
from pygraphon import graphon_graphon_product
A = graphon_product.draw(n = 100)

# Create a histogram estimator
estimator = HistogramEstimator()

# Fit the estimator to a graph with adjacency matrix A
estimator.fit(A)

# Get the estimated graphon
graphon_estimated = estimator.get_graphon()

# get the estimated block connectivity matrix
theta = graphon_estimated.get_theta()

# get the estimated edge probability matrix
P_estimated = graphon_estimated.get_edge_connectivity()

To cite this library, please use the following citation:

@misc{dufour_verdeyme_2023,
         author       = {Dufour, Charles and Verdeyme, Arthur},
         title        = {PyGraphon},
         year         = {2023},
         howpublished = {https://doi.org/10.5281/zenodo.10355247},
         note         = {Zenodo},
         doi          = {10.5281/zenodo.10355247}
    }

🚀 Installation

See Installation instructions

The most recent release can be installed from [PyPI](https://pypi.org/project/pygraphon/) with:

$ pip install pygraphon

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The most recent code and data can be installed directly from GitHub with:

$ pip install git+https://github.com/dufourc1/pygraphon.git

To install in development mode, use the following:

$ git clone git+https://github.com/dufourc1/pygraphon.git
$ cd pygraphon
$ pip install -e .

👐 Contributing

Contributions, whether filing an issue, making a pull request, or forking, are appreciated. See CONTRIBUTING.rst for more information on getting involved.

👋 Attribution

See Attributions

⚖️ License

The code in this package is licensed under the MIT License.

🍪 Cookiecutter

This package was created with @audreyfeldroy's cookiecutter package using @cthoyt's cookiecutter-snekpack template.

🛠️ For Developers

See developer instructions

The final section of the README is for if you want to get involved by making a code contribution.

❓ Testing

After cloning the repository and installing uv (see uv installation), the unit tests in the tests/ folder can be run reproducibly with:

$ uv run pytest tests

Additionally, these tests are automatically re-run with each commit in a GitHub Action.

### 📝 Documentation

The documentation is built with Sphinx. After installing the package in development mode, the documentation can be built locally with:

$ cd docs
$ make html

The documentation will then be available in docs/build/html/.

To build the documentation, you'll need to install the necessary dependencies. This can be done with:

$ uv sync --all-extras

note: to correctly format the documentation, one can use tool such as rstfmt (installable with uv pip install rstfmt).