Community detection using Newman spectral methods to maximize modularity
Project description
- 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 can be found .. _here: 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:
10-20-2017 Updated python codes to use NetworkX 2 APIs. See https://networkx.github.io/documentation/stable/release/release_2.0.html.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
Built Distribution
Hashes for python_modularity_maximization-0.0.1rc2-py2-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 0a3166feec14510a8409bc046e3a7d7305e14ee22a74009793be0b62fd0d8686 |
|
MD5 | 9f546ef53d3907aede19a104150b5cf6 |
|
BLAKE2b-256 | bd3e08c44373ab3376c56b4058fc478a21537ea748e481d06f0b009712ea0ec7 |