Welcome to scikit-network’s documentation!

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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


Quick start

Install scikit-network:

$ pip install scikit-network

Import scikit-network:

import sknetwork


An overview of the package is presented in this notebook.


The documentation is structured as follows:

  • Getting started: First steps to install, import and use scikit-network.

  • User manual: Description of each function and object of scikit-network.

  • Tutorials: Application of the main tools to toy examples.

  • Examples: Examples combining several tools on specific use cases.

  • About: Authors, history of the library, how to contribute, index of functions and objects.


If you want to cite scikit-network, please refer to the publication in the Journal of Machine Learning Research:

  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}

Getting started