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International Symposium on Experimental Algorithms

SEA 2009: Experimental Algorithms pp 257–268Cite as

Multi-level Algorithms for Modularity Clustering

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 5526)

Abstract

Modularity is a widely used quality measure for graph clusterings. Its exact maximization is prohibitively expensive for large graphs. Popular heuristics progressively merge clusters starting from singletons (coarsening), and optionally improve the resulting clustering by moving vertices between clusters (refinement). This paper experimentally compares existing and new heuristics of this type with respect to their effectiveness (achieved modularity) and runtime. For coarsening, it turns out that the most widely used criterion for merging clusters (modularity increase) is outperformed by other simple criteria, and that a recent multi-step algorithm is no improvement over simple single-step coarsening for these criteria. For refinement, a new multi-level algorithm produces significantly better clusterings than conventional single-level algorithms. A comparison with published benchmark results and algorithm implementations shows that combinations of coarsening and multi-level refinement are competitive with the best algorithms in the literature.

Keywords

  • Reduction Factor
  • Physical Review
  • Edge Weight
  • Community Detection
  • Graph Cluster

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Author information

Authors and Affiliations

  1. Brandenburg University of Technology, 03013, Cottbus, Germany

    Andreas Noack & Randolf Rotta

Authors
  1. Andreas Noack
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  2. Randolf Rotta
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Editor information

Editors and Affiliations

  1. Fakultät für Informatik, Informatik XI, Technische Universität Dortmund, Otto-Hahn-Straße 14, 44227, Dortmund, Germany

    Jan Vahrenhold

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Noack, A., Rotta, R. (2009). Multi-level Algorithms for Modularity Clustering. In: Vahrenhold, J. (eds) Experimental Algorithms. SEA 2009. Lecture Notes in Computer Science, vol 5526. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02011-7_24

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  • DOI: https://doi.org/10.1007/978-3-642-02011-7_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02010-0

  • Online ISBN: 978-3-642-02011-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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