https://github.com/zhiyzuo/python-modularity-maximization

Python implementation of Newman's spectral methods to maximize modularity.
https://github.com/zhiyzuo/python-modularity-maximization

community-detection-algorithm network-analysis python

Last synced: 3 months ago
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Python implementation of Newman's spectral methods to maximize modularity.

• Host: GitHub
• URL: https://github.com/zhiyzuo/python-modularity-maximization
• Owner: zhiyzuo
• License: mit
• Created: 2017-04-10T22:26:50.000Z (about 9 years ago)
• Default Branch: master
• Last Pushed: 2019-04-29T17:40:01.000Z (about 7 years ago)
• Last Synced: 2024-08-09T03:07:20.416Z (almost 2 years ago)
• Topics: community-detection-algorithm, network-analysis, python
• Language: Python
• Size: 1000 KB
• Stars: 45
• Watchers: 3
• Forks: 25
• Open Issues: 3
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE

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README

          
Python implementation of Newman's spectral methods to maximize modularity.

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See:

    - Leicht, E. A., & Newman, M. E. J. (2008). Community Structure in Directed Networks. Physical Review Letters, 100(11), 118703. https://doi.org/10.1103/PhysRevLett.100.118703

    - Newman, M. E. J. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences of the United States of America, 103(23), 8577–82. https://doi.org/10.1073/pnas.0601602103

A quick start: https://zhiyzuo.github.io/python-modularity-maximization/

All the datasets in ``./data`` comes from http://www-personal.umich.edu/~mejn/netdata/

Specifically, ``big_10_football_directed.gml`` is compiled by myself to test community detection for directed network. I combined data from http://www.sports-reference.com/cfb/conferences/big-ten/2005-schedule.html and the original ``football.gml`` to define the edge directions.

Change log:

- 02-23-2018

  Test on Python 3

- 10-20-2017

  Updated python codes to use NetworkX 2 APIs. See https://networkx.github.io/documentation/stable/release/release_2.0.html.

  Later in the day, I added a wrapper function to retrieve the largest eigenvalue and vector for 2x2 matrices since scipy.sparse.linalg.eigs do not work in that case.