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An OpenSource Python package for the extraction of fine-grained and time-stamped co-editing networks from git repositories.

Project description


git2net is an Open Source Python package that facilitates the extraction of co-editing networks from git repositories.

Download and installation

git2net is pure python code. It has no platform-specific dependencies and thus works on all platforms. The only requirement is a version of git >= 2.0. Assuming you are using pip, you can install latest version of git2net by running:

> pip install git2net

This also installs the necessary dependencies. git2net depends on the python-Levenshtein package to compute Levenshtein distances for edited lines of code. On sytems running Windows, automatically compiling this C based module might fail during installation. In this case, unofficial Windows binaries can be found here, which might help you get started.

How to use git2net

After installation, we suggest to check out our tutorial, detailing how to get started using git2net. We also provide detailed inline documentation serving as reference.

In addition, we have publised some motivating results as well as details on the mining algorithm in "git2net - Mining Time-Stamped Co-Editing Networks from Large git Repositories". Together with the paper, we have further released a jupyter notebook (using an early version of git2net) reproducing the majority of the results shown in the paper on

All functions of git2nethave been tested on Ubuntu, Mac OS, and Windows.

How to cite git2net

  title={git2net: mining time-stamped co-editing networks from large git repositories},
  author={Gote, Christoph and Scholtes, Ingo and Schweitzer, Frank},
  booktitle={Proceedings of the 16th International Conference on Mining Software Repositories},
  organization={IEEE Press}


This software is licensed under the GNU Affero General Public License v3 (AGPL-3.0).

Project details

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Files for git2net, version 1.4.9
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Filename, size git2net-1.4.9.tar.gz (26.7 kB) File type Source Python version None Upload date Hashes View

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