Post-Processing Hierarchical Community Structures: Quality Improvements and Multi-scale View
<|reference_start|>Post-Processing Hierarchical Community Structures: Quality Improvements and Multi-scale View: Dense sub-graphs of sparse graphs (communities), which appear in most real-world complex networks, play an important role in many contexts. Most existing community detection algorithms produce a hierarchical structure of community and seek a partition into communities that optimizes a given quality function. We propose new methods to improve the results of any of these algorithms. First we show how to optimize a general class of additive quality functions (containing the modularity, the performance, and a new similarity based quality function we propose) over a larger set of partitions than the classical methods. Moreover, we define new multi-scale quality functions which make it possible to detect the different scales at which meaningful community structures appear, while classical approaches find only one partition.<|reference_end|>
@article{pons2006post-processing,
    title={Post-Processing Hierarchical Community Structures: Quality Improvements
  and Multi-scale View},
    author={Pascal Pons and Matthieu Latapy},
    journal={Theoretical Computer Science, volume 412, issues 8-10, 4 March
  2011, pages 892-900},
    year={2006},
    doi={10.1016/j.tcs.2010.11.041},
    archivePrefix={arXiv},
    eprint={cs/0608050},
    primaryClass={cs.DS cond-mat.dis-nn physics.soc-ph}
}