Understanding Latent Interactions in Online Social Networks
Jiang, J.; Wilson, C.; Wang, X.; Sha, W.; Huang, P.; Dai, Y. & Zhao, B. Y.
ACM Trans. Web, 7(4) 18:1-18:39 (2013) [pdf]
Popular online social networks (OSNs) like Facebook and Twitter are changing the way users communicate and interact with the Internet. A deep understanding of user interactions in OSNs can provide important insights into questions of human social behavior and into the design of social platforms and applications. However, recent studies have shown that a majority of user interactions on OSNs are latent interactions, that is, passive actions, such as profile browsing, that cannot be observed by traditional measurement techniques. In this article, we seek a deeper understanding of both active and latent user interactions in OSNs. For quantifiable data on latent user interactions, we perform a detailed measurement study on Renren, the largest OSN in China with more than 220 million users to date. All friendship links in Renren are public, allowing us to exhaustively crawl a connected graph component of 42 million users and 1.66 billion social links in 2009. Renren also keeps detailed, publicly viewable visitor logs for each user profile. We capture detailed histories of profile visits over a period of 90 days for users in the Peking University Renren network and use statistics of profile visits to study issues of user profile popularity, reciprocity of profile visits, and the impact of content updates on user popularity. We find that latent interactions are much more prevalent and frequent than active events, are nonreciprocal in nature, and that profile popularity is correlated with page views of content rather than with quantity of content updates. Finally, we construct latent interaction graphs as models of user browsing behavior and compare their structural properties, evolution, community structure, and mixing times against those of both active interaction graphs and social graphs.
Ubicon and its Applications for Ubiquitous Social Computing
Atzmueller, M.; Becker, M.; Kibanov, M.; Scholz, C.; Doerfel, S.; Hotho, A.; Macek, B.-E.; Mitzlaff, F.; Mueller, J. & Stumme, G.
New Review of Hypermedia and Multimedia, 20(1) 53-77 (2014) [pdf]
The combination of ubiquitous and social computing is an emerging
esearch area which integrates different but complementary methods,
echniques and tools. In this paper, we focus on the Ubicon platform,
ts applications, and a large spectrum of analysis results.

bicon provides an extensible framework for building and hosting applications
argeting both ubiquitous and social environments. We summarize the
rchitecture and exemplify its implementation using four real-world
pplications built on top of Ubicon. In addition, we discuss several
cientific experiments in the context of these applications in order
o give a better picture of the potential of the framework, and discuss
nalysis results using several real-world data sets collected utilizing
Text Mining Scientific Papers: A Survey on FCA-Based Information Retrieval Research
Poelmans, J.; Ignatov, D.; Viaene, S.; Dedene, G. & Kuznetsov, S.
Perner, P., ed., 'Advances in Data Mining. Applications and Theoretical Aspects', 7377(), Springer Berlin Heidelberg, 273-287 (2012) [pdf]
Formal Concept Analysis (FCA) is an unsupervised clustering technique and many scientific papers are devoted to applying FCA in Information Retrieval (IR) research. We collected 103 papers published between 2003-2009 which mention FCA and information retrieval in the abstract, title or keywords. Using a prototype of our FCA-based toolset CORDIET, we converted the pdf-files containing the papers to plain text, indexed them with Lucene using a thesaurus containing terms related to FCA research and then created the concept lattice shown in this paper. We visualized, analyzed and explored the literature with concept lattices and discovered multiple interesting research streams in IR of which we give an extensive overview. The core contributions of this paper are the innovative application of FCA to the text mining of scientific papers and the survey of the FCA-based IR research.
Proceedings of the 1st International Workshop on Interactions between
Data Mining and Natural Language Processing co-located with The European
Conference on Machine Learning and Principles and Practice of Knowledge
Discovery in Databases, DMNLP@PKDD/ECML 2014, Nancy, France, September
15, 2014
2014, Cellier, P.; Charnois, T.; Hotho, A.; Matwin, S.; Moens, M. & Toussaint, Y., ed., 1202(), [pdf]
On the Semantics of User Interaction in Social Media (Extended Abstract, Resubmission)
Mitzlaff, F.; Atzmueller, M.; Stumme, G. & Hotho, A.
, 'Proc. LWA 2013 (KDML Special Track)', University of Bamberg, Bamberg, Germany (2011)
On the Semantics of User Interaction in Social Media (Extended Abstract, Resubmission)
Mitzlaff, F.; Atzmueller, M.; Stumme, G. & Hotho, A.
, 'Proc. LWA 2013 (KDML Special Track)', University of Bamberg, Bamberg, Germany (2011)
On the Evolution of Contacts and Communities in Networks of Face-to-Face Proximity
Kibanov, M.; Atzmueller, M.; Scholz, C. & Stumme, G.
, 'Proc. IEEE CPSCom 2013', IEEE Computer Society, Boston, MA, USA (2013)
Mining Videos from the Web for Electronic Textbooks
Agrawal, R.; Christoforaki, M.; Gollapudi, S.; Kannan, A.; Kenthapadi, K. & Swaminathan, A.
Glodeanu, C.; Kaytoue, M. & Sacarea, C., ed., 'Formal Concept Analysis', 8478(), Springer International Publishing, 219-234 (2014) [pdf]
We propose a system for mining videos from the web for supplementing the content of electronic textbooks in order to enhance their utility. Textbooks are generally organized into sections such that each section explains very few concepts and every concept is primarily explained in one section. Building upon these principles from the education literature and drawing upon the theory of
How Do People Link? Analysis of Contact Structures in Human Face-to-Face Proximity Networks
Scholz, C.; Atzmueller, M.; Kibanov, M. & Stumme, G.
, 'Proc. ASONAM 2013', ACM Press, New York, NY, USA (2013)
Exploratory Pattern Mining on Social Media using Geo-References and Social Tagging Information
Atzmueller, M. & Lemmerich, F.
International Journal of Web Science (Special Issue on Social Web Search and Mining), 2(1/2) (2013)