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# graspologic
[](http://www.jmlr.org/papers/volume20/19-490/19-490.pdf)
[](https://pypi.org/project/graspologic/)
[](https://pepy.tech/project/graspologic)
[](https://github.com/graspologic-org/graspologic/actions/workflows/build.yml)
[](https://opensource.org/licenses/MIT)
## `graspologic` is a package for graph statistical algorithms.
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- [Overview](#overview)
- [Documentation](#documentation)
- [System Requirements](#system-requirements)
- [Installation Guide](#installation-guide)
- [Contributing](#contributing)
- [License](#license)
- [Issues](#issues)
- [Citing `graspologic`](#citing-graspologic)
# Overview
A graph, or network, provides a mathematically intuitive representation of data with some sort of relationship between items. For example, a social network can be represented as a graph by considering all participants in the social network as nodes, with connections representing whether each pair of individuals in the network are friends with one another. Naively, one might apply traditional statistical techniques to a graph, which neglects the spatial arrangement of nodes within the network and is not utilizing all of the information present in the graph. In this package, we provide utilities and algorithms designed for the processing and analysis of graphs with specialized graph statistical algorithms.
# Documentation
The official documentation with usage is at [https://graspologic-org.github.io/graspologic/latest](https://graspologic-org.github.io/graspologic/latest)
Please visit the [tutorial section](https://graspologic-org.github.io/graspologic/latest/tutorials/index.html) in the official website for more in depth usage.
# System Requirements
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## Hardware requirements
`graspologic` package requires only a standard computer with enough RAM to support the in-memory operations.
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## Software requirements
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### OS Requirements
`graspologic` is tested on the following OSes:
- Linux x64
- macOS x64
- Windows 10 x64
And across the following **x86_64** versions of Python:
- 3.9
- 3.10
- 3.11
- 3.12
If you try to use `graspologic` for a different platform than the ones listed and notice any unexpected behavior,
please feel free to [raise an issue](https://github.com/graspologic-org/graspologic/issues/new). It's better for ourselves and our users
if we have concrete examples of things not working!
# Installation Guide
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## Install from pip
```
pip install graspologic
```
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## Install from Github
```
git clone https://github.com/graspologic-org/graspologic
cd graspologic
python3 -m venv venv
source venv/bin/activate
python3 setup.py install
```
# Contributing
We welcome contributions from anyone. Please see our [contribution guidelines](https://github.com/graspologic-org/graspologic/blob/dev/CONTRIBUTING.md) before making a pull request. Our
[issues](https://github.com/graspologic-org/graspologic/issues) page is full of places we could use help!
If you have an idea for an improvement not listed there, please
[make an issue](https://github.com/graspologic-org/graspologic/issues/new) first so you can discuss with the developers.
# License
This project is covered under the MIT License.
# Issues
We appreciate detailed bug reports and feature requests (though we appreciate pull requests even more!). Please visit our [issues](https://github.com/graspologic-org/graspologic/issues) page if you have questions or ideas.
# Citing `graspologic`
If you find `graspologic` useful in your work, please cite the package via the [GraSPy paper](http://www.jmlr.org/papers/volume20/19-490/19-490.pdf)
> Chung, J., Pedigo, B. D., Bridgeford, E. W., Varjavand, B. K., Helm, H. S., & Vogelstein, J. T. (2019). GraSPy: Graph Statistics in Python. Journal of Machine Learning Research, 20(158), 1-7.
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"description": "<!-- omit in toc -->\n# graspologic\n[](http://www.jmlr.org/papers/volume20/19-490/19-490.pdf)\n[](https://pypi.org/project/graspologic/)\n[](https://pepy.tech/project/graspologic)\n[](https://github.com/graspologic-org/graspologic/actions/workflows/build.yml)\n[](https://opensource.org/licenses/MIT)\n\n## `graspologic` is a package for graph statistical algorithms.\n<!-- no toc -->\n- [Overview](#overview)\n- [Documentation](#documentation)\n- [System Requirements](#system-requirements)\n- [Installation Guide](#installation-guide)\n- [Contributing](#contributing)\n- [License](#license)\n- [Issues](#issues)\n- [Citing `graspologic`](#citing-graspologic)\n\n# Overview\nA graph, or network, provides a mathematically intuitive representation of data with some sort of relationship between items. For example, a social network can be represented as a graph by considering all participants in the social network as nodes, with connections representing whether each pair of individuals in the network are friends with one another. Naively, one might apply traditional statistical techniques to a graph, which neglects the spatial arrangement of nodes within the network and is not utilizing all of the information present in the graph. In this package, we provide utilities and algorithms designed for the processing and analysis of graphs with specialized graph statistical algorithms.\n\n# Documentation\nThe official documentation with usage is at [https://graspologic-org.github.io/graspologic/latest](https://graspologic-org.github.io/graspologic/latest)\n\nPlease visit the [tutorial section](https://graspologic-org.github.io/graspologic/latest/tutorials/index.html) in the official website for more in depth usage.\n\n# System Requirements\n<!-- omit in toc -->\n## Hardware requirements\n`graspologic` package requires only a standard computer with enough RAM to support the in-memory operations.\n\n<!-- omit in toc -->\n## Software requirements\n<!-- omit in toc -->\n### OS Requirements\n`graspologic` is tested on the following OSes:\n- Linux x64\n- macOS x64\n- Windows 10 x64\n\nAnd across the following **x86_64** versions of Python:\n- 3.9\n- 3.10\n- 3.11\n- 3.12\n\nIf you try to use `graspologic` for a different platform than the ones listed and notice any unexpected behavior,\nplease feel free to [raise an issue](https://github.com/graspologic-org/graspologic/issues/new). It's better for ourselves and our users\nif we have concrete examples of things not working!\n\n# Installation Guide\n<!-- omit in toc -->\n## Install from pip\n```\npip install graspologic\n```\n\n<!-- omit in toc -->\n## Install from Github\n```\ngit clone https://github.com/graspologic-org/graspologic\ncd graspologic\npython3 -m venv venv\nsource venv/bin/activate\npython3 setup.py install\n```\n\n# Contributing\nWe welcome contributions from anyone. Please see our [contribution guidelines](https://github.com/graspologic-org/graspologic/blob/dev/CONTRIBUTING.md) before making a pull request. Our\n[issues](https://github.com/graspologic-org/graspologic/issues) page is full of places we could use help!\nIf you have an idea for an improvement not listed there, please\n[make an issue](https://github.com/graspologic-org/graspologic/issues/new) first so you can discuss with the developers.\n\n# License\nThis project is covered under the MIT License.\n\n# Issues\nWe appreciate detailed bug reports and feature requests (though we appreciate pull requests even more!). Please visit our [issues](https://github.com/graspologic-org/graspologic/issues) page if you have questions or ideas.\n\n# Citing `graspologic`\nIf you find `graspologic` useful in your work, please cite the package via the [GraSPy paper](http://www.jmlr.org/papers/volume20/19-490/19-490.pdf)\n\n> Chung, J., Pedigo, B. D., Bridgeford, E. W., Varjavand, B. K., Helm, H. S., & Vogelstein, J. T. (2019). GraSPy: Graph Statistics in Python. Journal of Machine Learning Research, 20(158), 1-7.\n\n",
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