Metadata-Version: 1.1
Name: python-modularity-maximization
Version: 0.0.1
Summary: Community detection using Newman spectral methods to maximize modularity
Home-page: http://zhiyzuo.github.io/python-modularity-maximization/
Author: Zhiya Zuo
Author-email: zhiyazuo@gmail.com
License: MIT
Download-URL: https://github.com/zhiyzuo/python-modularity-maximization/tarball/0.0.1
Description: Python implementation of Newman's spectral methods to maximize modularity.
        
        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
        
        
        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.
        
Keywords: modularity newman community-detection network-analysis clustering
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Information Technology
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
