Engineering Comparators for Graph Clusterings

  • Daniel Delling
  • Marco Gaertler
  • Robert Görke
  • Dorothea Wagner
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5034)


A promising approach to compare two graph clusterings is based on using measurements for calculating the distance between them. Existing measures either use the structure of clusterings or quality-based aspects with respect to some index evaluating both clusterings. Each approach suffers from conceptional drawbacks. We introduce a new approach combining both aspects and leading to better results for comparing graph clusterings. An experimental evaluation of existing and new measures shows that the significant drawbacks of existing techniques are not only theoretical in nature but manifest frequently on different types of graphs. The evaluation also proves that the results of our new measures are highly coherent with intuition, while avoiding the former weaknesses.


Mutual Information Regular Graph Confusion Matrix Qualitative Aspect Random 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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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Daniel Delling
    • 1
  • Marco Gaertler
    • 1
  • Robert Görke
    • 1
  • Dorothea Wagner
    • 1
  1. 1.Faculty of InformaticsUniversität Karlsruhe (TH) 

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