Welcome to CHAMP’s documentation!

Contents:

• Background
  • Introduction
  • Modularity
  • Multilayer CHAMP
• Running CHAMP
  • Starting from Partitions
  • Starting from Partition Coefficients
• Visualizing Results
  • Single Layer Plots
  • Multiayer Plots
• Louvain Parallel Extension
  • Partition Ensemble Example

Download and Installation:

The CHAMP module is hosted on PyPi. The easiest way to install is via the pip command:

pip install champ

For installation from source, the latest version of champ can be downloaded from GitHub:

https://github.com/wweir827/CHAMP

For basic installation:

python setup.py install

Dependencies

Most of the dependencies for CHAMP are fairly standard tools for data analysis in Python, with the exception of louvain_igraph. They include :

• NumPy
• sklearn
• igraph
• matplotlib
• louvain

Citation

Please cite:

[1]	William H. Weir, Scott Emmons, Ryan Gibson, Dane Taylor, and Peter J Mucha. Post-processing partitions to identify domains of modularity optimization. arXiv.org, 2017. arXiv:1706.03675.

bibtex

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