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 evidence networks using 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 evidence networks using user data from the real-world social
okmarking application BibSonomy. The results indicate that the evidence
tworks reflect the relative rating of the explicit ones very well.

@inproceedings{mitzlaff2010community, author = {Mitzlaff, Folke and Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, title = {Community Assessment using Evidence Networks}, booktitle = {Proceedings of the Workshop on Mining Ubiquitous and Social Environments (MUSE2010)}, address = {Barcelona, Spain}, year = {2010}, url = {http://www.kde.cs.uni-kassel.de/ws/muse2010}, keywords = {2010, assessment, community, ecml, evaluation, evidence, itegpub, muse, myown, networks, pkdd}, 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 evidence networks using 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 evidence networks using user data from the real-world social
okmarking application BibSonomy. The results indicate that the evidence
tworks reflect the relative rating of the explicit ones very well.} }

%0 = inproceedings %A = Mitzlaff, Folke and Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd %B = Proceedings of the Workshop on Mining Ubiquitous and Social Environments (MUSE2010) %C = Barcelona, Spain %D = 2010 %T = Community Assessment using Evidence Networks %U = http://www.kde.cs.uni-kassel.de/ws/muse2010