Mitzlaff, F.; Atzmueller, M.; Benz, D.; Hotho, A. & Stumme, G. (2013), 'User-Relatedness and Community Structure in Social Interaction Networks' , cite arxiv:1309.3888 .
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With social media and the according social and ubiquitous applications finding their way into everyday life, there is a rapidly growing amount of user generated content yielding explicit and implicit network structures. We consider social activities and phenomena as proxies for user relatedness. Such activities are represented in so-called social interaction networks or evidence networks, with different degrees of explicitness. We focus on evidence networks containing relations on users, which are represented by connections between individual nodes. Explicit interaction networks are then created by specific user actions, for example, when building a friend network. On the other hand, more implicit networks capture user traces or evidences of user actions as observed in Web portals, blogs, resource sharing systems, and many other social services. These implicit networks can be applied for a broad range of analysis methods instead of using expensive gold-standard information. In this paper, we analyze different properties of a set of networks in social media. We show that there are dependencies and correlations between the networks. These allow for drawing reciprocal conclusions concerning pairs of networks, based on the assessment of structural correlations and ranking interchangeability. Additionally, we show how these inter-network correlations can be used for assessing the results of structural analysis techniques, e.g., community mining methods.

Mitzlaff, F.; Atzmueller, M.; Benz, D.; Hotho, A. & Stumme, G. (2013), 'User-Relatedness and Community Structure in Social Interaction Networks', CoRR/abs 1309.3888 .
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Atzmueller, M.; Becker, M.; Kibanov, M.; Scholz, C.; Doerfel, S.; Hotho, A.; Macek, B.-E.; Mitzlaff, F.; Mueller, J. & Stumme, G. (2014), 'Ubicon and its Applications for Ubiquitous Social Computing', New Review of Hypermedia and Multimedia 1 (20) , 53--77 .
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The combination of ubiquitous and social computing is an emerging research area which integrates different but complementary methods, techniques and tools. In this paper, we focus on the Ubicon platform, its applications, and a large spectrum of analysis results. Ubicon provides an extensible framework for building and hosting applications targeting both ubiquitous and social environments. We summarize the architecture and exemplify its implementation using four real-world applications built on top of Ubicon. In addition, we discuss several scientific experiments in the context of these applications in order to give a better picture of the potential of the framework, and discuss analysis results using several real-world data sets collected utilizing Ubicon.

2013

Atzmueller, M. & Hilgenberg, K. (2013), Towards Capturing Social Interactions with SDCF: An Extensible Framework for Mobile Sensing and Ubiquitous Data Collection, in 'Proc. 4th International Workshop on Modeling Social Media (MSM 2013), Hypertext 2013' , ACM Press, New York, NY, USA .
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2014

Mitzlaff, F.; Atzmueller, M.; Hotho, A. & Stumme, G. (2014), 'The social distributional hypothesis: a pragmatic proxy for homophily in online social networks', Social Network Analysis and Mining 4 (1) .
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Applications of the Social Web are ubiquitous and have become an integral part of everyday life: Users make friends, for example, with the help of online social networks, share thoughts via Twitter, or collaboratively write articles in Wikipedia. All such interactions leave digital traces; thus, users participate in the creation of heterogeneous, distributed, collaborative data collections. In linguistics, the

2013

Atzmueller, M. (2013), Social Behavior in Mobile Social Networks: Characterizing Links, Roles and Communities, in Alvin Chin & Daqing Zhang, ed., 'Mobile Social Networking: An Innovative Approach' , Springer Verlag, Heidelberg, Germany .
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Mitzlaff, F.; Atzmueller, M.; Stumme, G. & Hotho, A. (2013), Semantics of User Interaction in Social Media, in Gourab Ghoshal; Julia Poncela-Casasnovas & Robert Tolksdorf, ed., 'Complex Networks IV' , Springer Verlag, Heidelberg, Germany .
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Schulz, T.; Skistims, H.; Zirfas, J.; Atzmueller, M. & Scholz, C. (2013), 'Rechtliche Ausgestaltung sozialer Konferenzplattformen', ZD 2 , 60--65 .
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Roth-Berghofer, T.; Oussena, S. & Atzmueller, M., ed. (2013), Proceedings of the 2013 International Smart University Workshop (SmartU 2013) , CONTEXT 2013 , Annecy, France .
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2012

Chin, A.; Atzmueller, M. & Helic, D., ed. (2012), Proceedings MSM 2012: Workshop on Modeling Social Media -- Collective Intelligence in Social Media , ACM Press , New York, NY, USA .
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2011

Mitzlaff, F.; Atzmueller, M.; Stumme, G. & Hotho, A. (2011), On the Semantics of User Interaction in Social Media (Extended Abstract, Resubmission), in 'Proc. LWA 2013 (KDML Special Track)' , University of Bamberg, Bamberg, Germany .
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2013

Kibanov, M.; Atzmueller, M.; Scholz, C. & Stumme, G. (2013), On the Evolution of Contacts and Communities in Networks of Face-to-Face Proximity, in 'Proc. IEEE CPSCom 2013' , IEEE Computer Society, Boston, MA, USA .
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2012

2013

Scholz, C.; Atzmueller, M.; Kibanov, M. & Stumme, G. (2013), How Do People Link? Analysis of Contact Structures in Human Face-to-Face Proximity Networks, in 'ASONAM' .
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Atzmueller, M. & Lemmerich, F. (2013), 'Exploratory Pattern Mining on Social Media using Geo-References and Social Tagging Information', International Journal of Web Science (Special Issue on Social Web Search and Mining) 2 (1/2) .
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Kibanov, M.; Atzmueller, M.; Scholz, C. & Stumme, G. (2013), Evolution of Contacts and Communities in Networks of Face-to-Face Proximity (Extended Abstract, Resubmission), in 'Proc. LWA 2013 (KDML Special Track)' , University of Bamberg, Bamberg, Germany .
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2012

Lemmerich, F. & Atzmueller, M. (2012), Describing Locations using Tags and Images: Explorative Pattern Mining in Social Media 'Modeling and Mining Ubiquitous Social Media' , Springer Verlag, Heidelberg, Germany .
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2013

Landia, N.; Doerfel, S.; Jäschke, R.; Anand, S. S.; Hotho, A. & Griffiths, N. (2013), 'Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations', cs.IR 1310.1498 .
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The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.

Atzmueller, M.; Kibanov, M.; Scholz, C. & Stumme, G. (2013), 'Conferator - a Social System for Conference and Contact Management' , Technical report, University of Koblenz-Landau , INFORMATIK 2013 , Poster at INFORMATIK 2013 .
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Doerfel, S. & Jäschke, R. (2013), An analysis of tag-recommender evaluation procedures, in 'Proceedings of the 7th ACM conference on Recommender systems' , ACM, New York, NY, USA , pp. 343--346 .
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Since the rise of collaborative tagging systems on the web, the tag recommendation task -- suggesting suitable tags to users of such systems while they add resources to their collection -- has been tackled. However, the (offline) evaluation of tag recommendation algorithms usually suffers from difficulties like the sparseness of the data or the cold start problem for new resources or users. Previous studies therefore often used so-called post-cores (specific subsets of the original datasets) for their experiments. In this paper, we conduct a large-scale experiment in which we analyze different tag recommendation algorithms on different cores of three real-world datasets. We show, that a recommender's performance depends on the particular core and explore correlations between performances on different cores.