We focus on face-to-face contact networks collected at different conferences using the social conference guidance system Conferator. Specifically, we investigate the strength of ties and its connection to triadic closures in face-to-face proximity networks. Furthermore, we analyze the predictability of all, new and recurring links at different points of time during the conference. In addition, we consider network dynamics for the prediction of new links. ER - TY - JOUR AU - Atzmueller, Martin T1 - Mining Social Media: Key Players, Sentiments, and Communities JO - WIREs: Data Mining and Knowledge Discovery PY - 2012/ VL - In Press IS - SP - EP - UR - M3 - KW - 2012 KW - analysis KW - community KW - data KW - detection KW - itegpub KW - mining KW - network KW - opinion KW - sentiment KW - social KW - venus KW - vikamine L1 - SN - N1 - N1 - AB - ER - TY - JOUR AU - Berendt, Bettina AU - Hotho, Andreas AU - Stumme, Gerd T1 - Bridging the Gap--Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0 JO - Web Semantics: Science, Services and Agents on the World Wide Web PY - 2010/ VL - 8 IS - 2-3 SP - 95 EP - 96 UR - http://www.sciencedirect.com/science/article/B758F-4YXK4HW-1/2/4cb514565477c54160b5e6eb716c32d7 M3 - DOI: 10.1016/j.websem.2010.04.008 KW - 2010 KW - data KW - introduction KW - itegpub KW - l3s KW - mining KW - myown KW - network KW - semantic KW - social KW - unik KW - web L1 - SN - N1 - N1 - AB - ER - TY - JOUR AU - BATAGELJ, VLADIMIR T1 - Social Network Analysis, Large-scale JO - Encyclopedia of Complexity and System Science PY - 2009/ VL - IS - SP - EP - UR - http://vlado.fmf.uni-lj.si/pub/networks/doc/mix/LargeSNA.pdf M3 - KW - analysis KW - batagelj KW - network KW - sna KW - social L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Brandes, Ulrik AU - Kenis, Patrick AU - Lerner, Jürgen AU - van Raaij, Denise A2 - T1 - Network analysis of collaboration structure in Wikipedia T2 - WWW '09: Proceedings of the 18th international conference on World wide web PB - ACM CY - New York, NY, USA PY - 2009/ M2 - VL - IS - SP - 731 EP - 740 UR - http://portal.acm.org/citation.cfm?id=1526808 M3 - http://doi.acm.org/10.1145/1526709.1526808 KW - analysis KW - collaboration KW - network KW - seminar2009 KW - sna KW - social KW - wikipedia L1 - SN - 978-1-60558-487-4 N1 - Network analysis of collaboration structure in Wikipedia N1 - AB - In this paper we give models and algorithms to describe and analyze the collaboration among authors of Wikipedia from a network analytical perspective. The edit network encodes who interacts how with whom when editing an article; it significantly extends previous network models that code author communities in Wikipedia. Several characteristics summarizing some aspects of the organization process and allowing the analyst to identify certain types of authors can be obtained from the edit network. Moreover, we propose several indicators characterizing the global network structure and methods to visualize edit networks. It is shown that the structural network indicators are correlated with quality labels of the associated Wikipedia articles. ER - TY - CONF AU - Breslin, John G. AU - Decker, Stefan AU - Hauswirth, Manfred AU - Hynes, Gearoid AU - Phuoc, Danh Le AU - Passant, Alexandre AU - Polleres, Axel AU - Rabsch, Cornelius AU - Reynolds, Vinny A2 - T1 - Integrating Social Networks and Sensor Networks T2 - Proceedings on the W3C Workshop on the Future of Social Networking PB - CY - PY - 2009/ M2 - VL - IS - SP - EP - UR - http://www.w3.org/2008/09/msnws/papers/sensors.html M3 - KW - network KW - networks KW - sensor KW - sensors KW - social KW - venus L1 - SN - N1 - N1 - AB - Sensors have begun to infiltrate people's everyday lives. They can provide information about a car's condition, can enable smart buildings, and are being used in various mobile applications, to name a few. Generally, sensors provide information about various aspects of the real world. Online social networks, another emerging trend over the past six or seven years, can provide insights into the communication links and patterns between people. They have enabled novel developments in communications as well as transforming the Web from a technical infrastructure to a social platform, very much along the lines of the original Web as proposed by Tim Berners-Lee, which is now often referred to as the Social Web. In this position paper, we highlight some of the interesting research areas where sensors and social networks can fruitfully interface, from sensors providing contextual information in context-aware and personalized social applications, to using social networks as "storage infrastructures" for sensor information. ER - TY - CONF AU - Das, Gautam AU - Koudas, Nick AU - Papagelis, Manos AU - Puttaswamy, Sushruth A2 - T1 - Efficient sampling of information in social networks T2 - SSM '08: Proceeding of the 2008 ACM workshop on Search in social media PB - ACM CY - New York, NY, USA PY - 2008/ M2 - VL - IS - SP - 67 EP - 74 UR - http://portal.acm.org/citation.cfm?id=1458583.1458594 M3 - http://doi.acm.org/10.1145/1458583.1458594 KW - analysis KW - network KW - networks KW - sampling KW - sna KW - social L1 - SN - 978-1-60558-258-0 N1 - Efficient sampling of information in social networks N1 - AB - As online social networking emerges, there has been increased interest to utilize the underlying social structure as well as the available social information to improve search. In this paper, we focus on improving the performance of information collection from the neighborhood of a user in a dynamic social network. To this end, we introduce sampling based algorithms to quickly approximate quantities of interest from the vicinity of a user's social graph. We then introduce and analyze variants of this basic scheme exploring correlations across our samples. Models of centralized and distributed social networks are considered. We show that our algorithms can be utilized to rank items in the neighborhood of a user, assuming that information for each user in the network is available. Using real and synthetic data sets, we validate the results of our analysis and demonstrate the efficiency of our algorithms in approximating quantities of interest. The methods we describe are general and can probably be easily adopted in a variety of strategies aiming to efficiently collect information from a social graph. ER - TY - CONF AU - Krause, Beate AU - Jäschke, Robert AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Logsonomy - Social Information Retrieval with Logdata T2 - HT '08: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia PB - ACM CY - New York, NY, USA PY - 2008/ M2 - VL - IS - SP - 157 EP - 166 UR - http://portal.acm.org/citation.cfm?id=1379092.1379123&coll=ACM&dl=ACM&type=series&idx=SERIES399&part=series&WantType=Journals&title=Proceedings%20of%20the%20nineteenth%20ACM%20conference%20on%20Hypertext%20and%20hypermedia M3 - http://doi.acm.org/10.1145/1379092.1379123 KW - 2.0 KW - 2008 KW - analysis KW - folksonomy KW - information KW - itegpub KW - logsonomy KW - myown KW - network KW - retrieval KW - search KW - social KW - tagorapub KW - web KW - web2.0 KW - web20 L1 - SN - 978-1-59593-985-2 N1 - N1 - AB - Social bookmarking systems constitute an established
part of the Web 2.0. In such systems
users describe bookmarks by keywords
called tags. The structure behind these social
systems, called folksonomies, can be viewed
as a tripartite hypergraph of user, tag and resource
nodes. This underlying network shows
specific structural properties that explain its
growth and the possibility of serendipitous
exploration.
Today’s search engines represent the gateway
to retrieve information from the World Wide
Web. Short queries typically consisting of
two to three words describe a user’s information
need. In response to the displayed
results of the search engine, users click on
the links of the result page as they expect
the answer to be of relevance.
This clickdata can be represented as a folksonomy
in which queries are descriptions of
clicked URLs. The resulting network structure,
which we will term logsonomy is very
similar to the one of folksonomies. In order
to find out about its properties, we analyze
the topological characteristics of the tripartite
hypergraph of queries, users and bookmarks
on a large snapshot of del.icio.us and
on query logs of two large search engines.
All of the three datasets show small world
properties. The tagging behavior of users,
which is explained by preferential attachment
of the tags in social bookmark systems, is
reflected in the distribution of single query
words in search engines. We can conclude
that the clicking behaviour of search engine
users based on the displayed search results
and the tagging behaviour of social bookmarking
users is driven by similar dynamics. ER - TY - CONF AU - Leskovec, Jure AU - Horvitz, Eric A2 - T1 - Planetary-scale views on a large instant-messaging network T2 - WWW '08: Proceeding of the 17th international conference on World Wide Web PB - ACM CY - New York, NY, USA PY - 2008/ M2 - VL - IS - SP - 915 EP - 924 UR - http://portal.acm.org/citation.cfm?id=1367620 M3 - http://doi.acm.org/10.1145/1367497.1367620 KW - analysis KW - messenger KW - msn KW - network KW - seminar2009 KW - sna KW - social L1 - SN - 978-1-60558-085-2 N1 - Planetary-scale views on a large instant-messaging network N1 - AB - We present a study of anonymized data capturing a month of high-level communication activities within the whole of the Microsoft Messenger instant-messaging system. We examine characteristics and patterns that emerge from the collective dynamics of large numbers of people, rather than the actions and characteristics of individuals. The dataset contains summary properties of 30 billion conversations among 240 million people. From the data, we construct a communication graph with 180 million nodes and 1.3 billion undirected edges, creating the largest social network constructed and analyzed to date. We report on multiple aspects of the dataset and synthesized graph. We find that the graph is well-connected and robust to node removal. We investigate on a planetary-scale the oft-cited report that people are separated by "six degrees of separation" and find that the average path length among Messenger users is 6.6. We find that people tend to communicate more with each other when they have similar age, language, and location, and that cross-gender conversations are both more frequent and of longer duration than conversations with the same gender. ER - TY - GEN AU - Noack, Andreas A2 - T1 - Modularity clustering is force-directed layout JO - PB - AD - PY - 2008/ VL - IS - SP - EP - UR - http://www.citebase.org/abstract?id=oai:arXiv.org:0807.4052 M3 - KW - clustering KW - communities KW - community KW - graph KW - layout KW - modularity KW - network KW - sna L1 - N1 - [0807.4052] Modularity clustering is force-directed layout N1 - AB - Two natural and widely used representations for the community structure of networks are clusterings, which partition the vertex set into disjoint subsets, and layouts, which assign the vertices to positions in a metric space. This paper unifies prominent characterizations of layout quality and clustering quality, by showing that energy models of pairwise attraction and repulsion subsume Newman and Girvan's modularity measure. Layouts with optimal energy are relaxations of, and are thus consistent with, clusterings with optimal modularity, which is of practical relevance because both representations are complementary and often used together. ER - TY - BOOK AU - Carrington, Peter J. A2 - T1 - Models and methods in social network analysis PB - Cambridge Univ. Press Cambridge [u.a.] AD - PY - 2007/ VL - IS - SP - EP - UR - http://opac.bibliothek.uni-kassel.de/DB=23/PPN?PPN=190999837 M3 - KW - Mathematisches KW - Modell KW - Netzwerkanalyse KW - Soziologie KW - analysis KW - methods KW - models KW - network KW - sna KW - social L1 - SN - 0-521-80959-2 N1 - UB Kassel N1 - AB - Literaturangaben ER - TY - JOUR AU - Cattuto, Ciro AU - Schmitz, Christoph AU - Baldassarri, Andrea AU - Servedio, Vito D. P. AU - Loreto, Vittorio AU - Hotho, Andreas AU - Grahl, Miranda AU - Stumme, Gerd T1 - Network Properties of Folksonomies JO - AI Communications Journal, Special Issue on ``Network Analysis in Natural Sciences and Engineering'' PY - 2007/ VL - 20 IS - 4 SP - 245 EP - 262 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2007/cattuto2007network.pdf M3 - KW - 2007 KW - emergent KW - fca KW - folksonomies KW - folksonomy KW - itegpub KW - l3s KW - myown KW - network KW - semantics KW - seminar2009 KW - tagorapub L1 - SN - N1 - Publications of Gerd Stumme N1 - AB - ER - TY - GEN AU - A2 - Alani, Harith A2 - Hoser, Bettina A2 - Schmitz, Christoph A2 - Stumme, Gerd T1 - Proceedings of the 2nd Workshop on Semantic Network Analysis JO - PB - AD - PY - 2006/ VL - IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/ws/sna2006/ M3 - KW - 2006 KW - Network KW - Semantic KW - analysis KW - eswc KW - l3s KW - myown KW - nepomuk KW - network KW - proceedings KW - semantic KW - workshop L1 - N1 - Publications of Gerd Stumme N1 - AB - ER - TY - CONF AU - Schmitz, Christoph AU - Hotho, Andreas AU - Jäschke, Robert AU - Stumme, Gerd A2 - Batagelj, V. A2 - Bock, H.-H. A2 - Ferligoj, A. A2 - Žiberna, A. T1 - Mining Association Rules in Folksonomies T2 - Data Science and Classification. Proceedings of the 10th IFCS Conf. PB - Springer CY - Heidelberg PY - 2006/07 M2 - VL - IS - SP - 261 EP - 270 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2006/schmitz2006mining.pdf M3 - KW - 2006 KW - analysis KW - fca KW - folksonomies KW - folksonomy KW - l3s KW - myown KW - nepomuk KW - network KW - semantic L1 - SN - N1 - Publications of Gerd Stumme N1 - AB - Social bookmark tools are rapidly emerging on the Web. In such
systems users are setting up lightweight conceptual structures
called folksonomies. These systems provide currently relatively few
structure. We discuss in this paper, how association rule mining
can be adopted to analyze and structure folksonomies, and how the results can be used
for ontology learning and supporting emergent semantics. We
demonstrate our approach on a large scale dataset stemming from an
online system. ER - TY - BOOK AU - Scott, John A2 - T1 - Social network analysis PB - Sage Publ. London [u.a.] AD - PY - 2005/ VL - IS - SP - EP - UR - http://opac.bibliothek.uni-kassel.de/DB=23/PPN?PPN=128222697 M3 - KW - Netzwerkanalyse KW - Soziologie KW - analysis KW - network KW - sna KW - social L1 - SN - 0-7619-6338-3 N1 - UB Kassel N1 - AB - Literaturverz. S. [193] - 204 ER - TY - GEN AU - A2 - Stumme, Gerd A2 - Hoser, Bettina A2 - Schmitz, Christoph A2 - Alani, Harith T1 - Proceedings of the First Workshop on Semantic Network Analysis JO - PB - CEUR Proceedings AD - Aachen PY - 2005/ VL - IS - SP - EP - UR - http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-171/ M3 - KW - 2005 KW - analysis KW - iswc KW - itegpub KW - l3s KW - myown KW - nepomuk KW - network KW - proceedings KW - semantic KW - semna KW - sna KW - workshop L1 - N1 - Publications of Gerd Stumme N1 - AB - ER - TY - CONF AU - Freeman, Linton C. A2 - Breiger, Ronald A2 - Carley, Kathleen A2 - Pattison, Philippa T1 - Finding Social Groups: A Meta-Analysis of the Southern Women Data T2 - Dynamic Social Network Modeling and Analysis: Workshop Summary and Papers PB - National Academies Press CY - PY - 2003/ M2 - VL - IS - SP - EP - UR - M3 - KW - analysis KW - network KW - sna KW - social KW - southern KW - women L1 - SN - N1 - N1 - AB - ER -