Community assessment using evidence networks

  • Authors:

  • Folke Mitzlaff;Martin Atzmueller;Dominik Benz;Andreas Hotho;Gerd Stumme

  • Affiliations:

  • University of Kassel, Knowledge and Data Engineering Group, Kassel, Germany;University of Kassel, Knowledge and Data Engineering Group, Kassel, Germany;University of Kassel, Knowledge and Data Engineering Group, Kassel, Germany;University of Wuerzburg, Data Mining and Information Retrieval Group, Wuerzburg, Germany;University of Kassel, Knowledge and Data Engineering Group, Kassel, Germany

  • Venue:
  • MSM'10/MUSE'10 Proceedings of the 2010 international conference on Analysis of social media and ubiquitous data
  • Year:
  • 2010

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Abstract

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.