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Free software library in Python for machine learning on graphs:

• Memory-efficient representation of graphs as sparse matrices in scipy_ format
• Fast algorithms
• Simple API inspired by scikit-learn_

.. _scipy: https://www.scipy.org .. _scikit-learn: https://scikit-learn.org/

Resources

• Free software: BSD license
• GitHub: https://github.com/sknetwork-team/scikit-network
• Documentation: https://scikit-network.readthedocs.io

Quick start

Install scikit-network:

.. code-block:: console

$ pip install scikit-network

Import scikit-network::

import sknetwork

Overview

An overview of the package is presented in this notebook <https://scikit-network.readthedocs.io/en/latest/tutorials/overview/index.html>_.

Documentation

The documentation is structured as follows:

• Getting started <https://scikit-network.readthedocs.io/en/latest/first_steps.html>_: First steps to install, import and use scikit-network.
• User manual <https://scikit-network.readthedocs.io/en/latest/reference/data.html>_: Description of each function and object of scikit-network.
• Tutorials <https://scikit-network.readthedocs.io/en/latest/tutorials/data/index.html>_: Application of the main tools to toy examples.
• Examples <https://scikit-network.readthedocs.io/en/latest/use_cases/text.html>_: Examples combining several tools on specific use cases.
• About <https://scikit-network.readthedocs.io/en/latest/authors.html>_: Authors, history of the library, how to contribute, index of functions and objects.

Citing

If you want to cite scikit-network, please refer to the publication in the Journal of Machine Learning Research <https://jmlr.org>_:

.. code::

@article{JMLR:v21:20-412,
  author  = {Thomas Bonald and Nathan de Lara and Quentin Lutz and Bertrand Charpentier},
  title   = {Scikit-network: Graph Analysis in Python},
  journal = {Journal of Machine Learning Research},
  year    = {2020},
  volume  = {21},
  number  = {185},
  pages   = {1-6},
  url     = {http://jmlr.org/papers/v21/20-412.html}
}