With social media and the according social and ubiquitous applications
nding their way into everyday life, there is a rapidly growing amount of user
nerated content yielding explicit and implicit network structures. We
nsider social activities and phenomena as proxies for user relatedness. Such
tivities are represented in so-called social interaction networks or evidence
tworks, with different degrees of explicitness. We focus on evidence networks
ntaining relations on users, which are represented by connections between
dividual nodes. Explicit interaction networks are then created by specific
er actions, for example, when building a friend network. On the other hand,
re implicit networks capture user traces or evidences of user actions as
served in Web portals, blogs, resource sharing systems, and many other social
rvices. These implicit networks can be applied for a broad range of analysis
thods instead of using expensive gold-standard information.
In this paper, we analyze different properties of a set of networks in social
dia. We show that there are dependencies and correlations between the
tworks. These allow for drawing reciprocal conclusions concerning pairs of
tworks, based on the assessment of structural correlations and ranking
terchangeability. Additionally, we show how these inter-network correlations
n be used for assessing the results of structural analysis techniques, e.g.,
mmunity mining methods.

@misc{mitzlaff2013userrelatedness, author = {Mitzlaff, Folke and Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, title = {User-Relatedness and Community Structure in Social Interaction Networks}, year = {2013}, note = {cite arxiv:1309.3888}, url = {http://arxiv.org/abs/1309.3888}, keywords = {2013, community, evidence, iteg, itegpub, l3s, myown, networks, social}, abstract = {With social media and the according social and ubiquitous applicationsfinding their way into everyday life, there is a rapidly growing amount of usergenerated content yielding explicit and implicit network structures. Weconsider social activities and phenomena as proxies for user relatedness. Suchactivities are represented in so-called social interaction networks or evidencenetworks, with different degrees of explicitness. We focus on evidence networkscontaining relations on users, which are represented by connections betweenindividual nodes. Explicit interaction networks are then created by specificuser actions, for example, when building a friend network. On the other hand,more implicit networks capture user traces or evidences of user actions asobserved in Web portals, blogs, resource sharing systems, and many other socialservices. These implicit networks can be applied for a broad range of analysismethods instead of using expensive gold-standard information. In this paper, we analyze different properties of a set of networks in socialmedia. We show that there are dependencies and correlations between thenetworks. These allow for drawing reciprocal conclusions concerning pairs ofnetworks, based on the assessment of structural correlations and rankinginterchangeability. Additionally, we show how these inter-network correlationscan be used for assessing the results of structural analysis techniques, e.g.,community mining methods.} }

%0 = misc %A = Mitzlaff, Folke and Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd %B = } %C = %D = 2013 %I = %T = User-Relatedness and Community Structure in Social Interaction Networks} %U = http://arxiv.org/abs/1309.3888

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.

@inproceedings{mitzlaff2011community, author = {Mitzlaff, Folke and Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, title = {Community Assessment Using Evidence Networks}, editor = {Atzmueller, Martin and Hotho, Andreas and Strohmaier, Markus and Chin, Alvin}, booktitle = {Analysis of Social Media and Ubiquitous Data}, series = {Lecture Notes in Computer Science}, publisher = {Springer Berlin Heidelberg}, year = {2011}, volume = {6904}, pages = {79-98}, url = {http://dx.doi.org/10.1007/978-3-642-23599-3_5}, doi = {10.1007/978-3-642-23599-3_5}, isbn = {978-3-642-23598-6}, keywords = {2011, COMMUNE, evaluation, evidence, myown, networks}, 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.} }

%0 = inproceedings %A = Mitzlaff, Folke and Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd %B = Analysis of Social Media and Ubiquitous Data %D = 2011 %I = Springer Berlin Heidelberg %T = Community Assessment Using Evidence Networks %U = http://dx.doi.org/10.1007/978-3-642-23599-3_5

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 Atzmüller, 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, bibsonomy, community, evaluation, evidence, itegpub, l3s, myown, networks}, 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 Atzmüller, 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

@inproceedings{eisterlehner2010visit, author = {Mitzlaff, Folke and Benz, Dominik and Stumme, Gerd and Hotho, Andreas}, title = {Visit me, click me, be my friend: An analysis of evidence networks of user relationships in Bibsonomy}, booktitle = {Proceedings of the 21st ACM conference on Hypertext and hypermedia}, address = {Toronto, Canada}, year = {2010}, keywords = {2010, analysis, bibsonomy, evidence, itegpub, l3s, links, myown, networks, semantic, sna, web} }

%0 = inproceedings %A = Mitzlaff, Folke and Benz, Dominik and Stumme, Gerd and Hotho, Andreas %B = Proceedings of the 21st ACM conference on Hypertext and hypermedia %C = Toronto, Canada %D = 2010 %T = Visit me, click me, be my friend: An analysis of evidence networks of user relationships in Bibsonomy