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Post-processing hierarchical community structures: Quality improvements and multi-scale view

Pascal Pons Matthieu Latapy 1
1 ComplexNetworks
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : 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 communities 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 which 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 different scales at which meaningful community structures appear, while classical approaches find only one partition.
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https://hal.archives-ouvertes.fr/hal-01146086
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Submitted on : Monday, April 27, 2015 - 4:02:08 PM
Last modification on : Monday, May 6, 2019 - 11:49:48 AM

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Pascal Pons, Matthieu Latapy. Post-processing hierarchical community structures: Quality improvements and multi-scale view. Theoretical Computer Science, Elsevier, 2011, 412 (8-10), pp.892-900. ⟨10.1016/j.tcs.2010.11.041⟩. ⟨hal-01146086⟩

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