Community Assessment Using Evidence Networks

  • Folke Mitzlaff
  • Martin Atzmueller
  • Dominik Benz
  • Andreas Hotho
  • Gerd Stumme
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6904)


Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups.

This paper proposes an approach for (relative) community assessment. We introduce a set of so-called evidence networks which are capturing typical interactions in social network applications. Thus, we are able to apply a rich set of implicit information for the evaluation of communities. The presented evaluation approach is based on the idea of reconstructing existing social structures for the assessment and evaluation of a given clustering. We analyze and compare the presented approach applying user data from the real-world social bookmarking application BibSonomy. The results indicate that the evidence networks reflect the relative rating of the explicit ones very well.


Latent Dirichlet Allocation Social Application Community Mining Community Assessment Social Bookmark 
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 2011

Authors and Affiliations

  • Folke Mitzlaff
    • 1
  • Martin Atzmueller
    • 1
  • Dominik Benz
    • 1
  • Andreas Hotho
    • 2
  • Gerd Stumme
    • 1
  1. 1.Knowledge and Data Engineering GroupUniversity of KasselKasselGermany
  2. 2.Data Mining and Information Retrieval GroupUniversity of WuerzburgWuerzburgGermany

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