Significance-Driven Graph Clustering

  • Marco Gaertler
  • Robert Görke
  • Dorothea Wagner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4508)


Modularity, the recently defined quality measure for clusterings, has attained instant popularity in the fields of social and natural sciences. We revisit the rationale behind the definition of modularity and explore the founding paradigm. This paradigm is based on the trade-off between the achieved quality and the expected quality of a clustering with respect to networks with similar intrinsic structure. We experimentally evaluate realizations of this paradigm systematically, including modularity, and describe efficient algorithms for their optimization. We confirm the feasibility of the resulting generality by a first systematic analysis of the behavior of these realizations on both artificial and on real-world data, arriving at remarkably good results of community detection.


Quality Index Greedy Algorithm Community Detection Graph Cluster Absolute Variant 
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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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Marco Gaertler
    • 1
  • Robert Görke
    • 1
  • Dorothea Wagner
    • 1
  1. 1.Faculty of Informatics, Universität Karlsruhe (TH) 

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