https://github.com/bagrow/linkcomm

Programs to identify link communities in complex networks
https://github.com/bagrow/linkcomm

Last synced: 2 months ago
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Programs to identify link communities in complex networks

• Host: GitHub
• URL: https://github.com/bagrow/linkcomm
• Owner: bagrow
• Created: 2010-09-11T22:07:29.000Z (almost 14 years ago)
• Default Branch: master
• Last Pushed: 2021-02-05T17:35:03.000Z (over 3 years ago)
• Last Synced: 2024-01-25T08:13:43.949Z (5 months ago)
• Language: Python
• Homepage:
• Size: 69.3 KB
• Stars: 91
• Watchers: 11
• Forks: 49
• Open Issues: 11
• Metadata Files:
  • Readme: README.markdown

Lists

• awesome-network-analysis - Linkcomm - Link Communities in Complex Networks - Community detection algorithms, available in C++, Python [and R](https://CRAN.R-project.org/package=linkcomm). (Software / Algorithms)
• awesome-network-analysis - Linkcomm - Link Communities in Complex Networks - Community detection algorithms, available in C++, Python [and R](https://cran.r-project.org/package=linkcomm). (Software / Algorithms)

README

        
About

=====

Here is the code for finding link communities [1] in complex networks.

Currently, we have two implementations: Python and C++. 

* Python: This implements the complete algorithm. It calculates

  similarities, constructs the dendrogram, and extracts the optimal

  communities. It is suitable for many occasions, is easy to use, and gives

  the exact solution. It may be too slow for very large networks, however,

  in which case you may want to use the C++ version.

* C++: to save computing time and memory usage, it calculates

  similarities but does not construct the dendrogram. You must manually 

  specify the similarity threshold used to obtain the communities.

References

==========

1. Yong-Yeol Ahn, James P. Bagrow, and Sune Lehmann, Link communities reveal

   multiscale complexity in networks, _Nature_ **466**, 761 (2010).