This page is about our paper
Scikit-network: Graph Analysis in Python
by Thomas Bonald, Nathan de Lara, Quentin Lutz and Bertrand Charpentier
Published at the Journal of Machine Learning Research (JMLR), 2020
Abstract
Scikit-network is a Python package inspired by scikit-learn for the analysis of large graphs. Graphs are represented by their adjacency matrix in the sparse CSR format of SciPy. The package provides state-of-the-art algorithms for ranking, clustering, classifying, embedding and visualizing the nodes of a graph. High performance is achieved through a mix of fast matrix-vector products (using SciPy), compiled code (using Cython) and parallel processing. The package is distributed under the BSD license, with dependencies limited to NumPy and SciPy. It is compatible with Python 3.6 and newer. Source code, documentation and installation instructions are available online
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