@incollection{atzmueller2013social, author = {Atzmueller, Martin}, title = {Social Behavior in Mobile Social Networks: Characterizing Links, Roles and Communities}, editor = {Chin, Alvin and Zhang, Daqing}, booktitle = {Mobile Social Networking: An Innovative Approach}, publisher = {Springer Verlag}, address = {Heidelberg, Germany}, year = {2013}, keywords = {2013, analysis, iteg, itegpub, l3s, media, network, social, venus} }

%0 = incollection %A = Atzmueller, Martin %B = Mobile Social Networking: An Innovative Approach %C = Heidelberg, Germany %D = 2013 %I = Springer Verlag %T = Social Behavior in Mobile Social Networks: Characterizing Links, Roles and Communities

@inproceedings{scholz2013people, author = {Scholz, Christoph and Atzmueller, Martin and Kibanov, Mark and Stumme, Gerd}, title = {How Do People Link? Analysis of Contact Structures in Human Face-to-Face Proximity Networks}, booktitle = {ASONAM}, year = {2013}, keywords = {2013, analysis, iteg, itegpub, l3s, link, myown, network, prediction, social} }

%0 = inproceedings %A = Scholz, Christoph and Atzmueller, Martin and Kibanov, Mark and Stumme, Gerd %B = ASONAM %D = 2013 %T = How Do People Link? Analysis of Contact Structures in Human Face-to-Face Proximity Networks

Understanding the process of link creation is rather important for link prediction in social networks. Therefore, this paper analyzes contact structures in networks of face-to-face spatial proximity, and presents new insights on the dynamic and static contact behavior in such real world networks.
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.

@inproceedings{scholz2013people, author = {Scholz, Christoph and Atzmueller, Martin and Kibanov, Mark and Stumme, Gerd}, title = {How Do People Link? Analysis of Contact Structures in Human Face-to-Face Proximity Networks}, booktitle = {Advances in Social Networks Analysis and Mining (ASONAM), 2013 International Conference on}, address = {Los Alamitos, CA, USA}, year = {2013}, keywords = {2013, analysis, iteg, itegpub, l3s, link, myown, network}, abstract = {Understanding the process of link creation is rather important for link prediction in social networks. Therefore, this paper analyzes contact structures in networks of face-to-face spatial proximity, and presents new insights on the dynamic and static contact behavior in such real world networks.
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.} }

%0 = inproceedings %A = Scholz, Christoph and Atzmueller, Martin and Kibanov, Mark and Stumme, Gerd %B = Advances in Social Networks Analysis and Mining (ASONAM), 2013 International Conference on %C = Los Alamitos, CA, USA %D = 2013 %T = How Do People Link? Analysis of Contact Structures in Human Face-to-Face Proximity Networks

@article{Atzmueller:12c, author = {Atzmueller, Martin}, title = {Mining Social Media: Key Players, Sentiments, and Communities}, journal = {WIREs: Data Mining and Knowledge Discovery}, year = {2012}, volume = {In Press}, keywords = {2012, analysis, community, data, detection, itegpub, mining, network, opinion, sentiment, social, venus, vikamine} }

%0 = article %A = Atzmueller, Martin %D = 2012 %T = Mining Social Media: Key Players, Sentiments, and Communities

@article{batagelj2009social, author = {BATAGELJ, VLADIMIR}, title = {Social Network Analysis, Large-scale}, journal = {Encyclopedia of Complexity and System Science}, year = {2009}, url = {http://vlado.fmf.uni-lj.si/pub/networks/doc/mix/LargeSNA.pdf}, keywords = {analysis, batagelj, network, sna, social} }

%0 = article %A = BATAGELJ, VLADIMIR %D = 2009 %T = Social Network Analysis, Large-scale %U = http://vlado.fmf.uni-lj.si/pub/networks/doc/mix/LargeSNA.pdf

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.

@inproceedings{1526808, author = {Brandes, Ulrik and Kenis, Patrick and Lerner, Jürgen and van Raaij, Denise}, title = {Network analysis of collaboration structure in Wikipedia}, booktitle = {WWW '09: Proceedings of the 18th international conference on World wide web}, publisher = {ACM}, address = {New York, NY, USA}, year = {2009}, pages = {731--740}, url = {http://portal.acm.org/citation.cfm?id=1526808}, doi = {http://doi.acm.org/10.1145/1526709.1526808}, isbn = {978-1-60558-487-4}, keywords = {analysis, collaboration, network, seminar2009, sna, social, wikipedia}, abstract = {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.} }

%0 = inproceedings %A = Brandes, Ulrik and Kenis, Patrick and Lerner, Jürgen and van Raaij, Denise %B = WWW '09: Proceedings of the 18th international conference on World wide web %C = New York, NY, USA %D = 2009 %I = ACM %T = Network analysis of collaboration structure in Wikipedia %U = http://portal.acm.org/citation.cfm?id=1526808

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.

@inproceedings{das2008efficient, author = {Das, Gautam and Koudas, Nick and Papagelis, Manos and Puttaswamy, Sushruth}, title = {Efficient sampling of information in social networks}, booktitle = {SSM '08: Proceeding of the 2008 ACM workshop on Search in social media}, publisher = {ACM}, address = {New York, NY, USA}, year = {2008}, pages = {67--74}, url = {http://portal.acm.org/citation.cfm?id=1458583.1458594}, doi = {http://doi.acm.org/10.1145/1458583.1458594}, isbn = {978-1-60558-258-0}, keywords = {analysis, network, networks, sampling, sna, social}, abstract = {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.} }

%0 = inproceedings %A = Das, Gautam and Koudas, Nick and Papagelis, Manos and Puttaswamy, Sushruth %B = SSM '08: Proceeding of the 2008 ACM workshop on Search in social media %C = New York, NY, USA %D = 2008 %I = ACM %T = Efficient sampling of information in social networks %U = http://portal.acm.org/citation.cfm?id=1458583.1458594

Social bookmarking systems constitute an established
rt of the Web 2.0. In such systems
ers describe bookmarks by keywords
lled tags. The structure behind these social
stems, called folksonomies, can be viewed
a tripartite hypergraph of user, tag and resource
des. This underlying network shows
ecific structural properties that explain its
owth and the possibility of serendipitous
ploration.
day’s search engines represent the gateway
retrieve information from the World Wide
b. Short queries typically consisting of
o to three words describe a user’s information
ed. In response to the displayed
sults of the search engine, users click on
e links of the result page as they expect
e answer to be of relevance.
is clickdata can be represented as a folksonomy
which queries are descriptions of
icked URLs. The resulting network structure,
ich we will term logsonomy is very
milar to the one of folksonomies. In order
find out about its properties, we analyze
e topological characteristics of the tripartite
pergraph of queries, users and bookmarks
a large snapshot of del.icio.us and
query logs of two large search engines.
l of the three datasets show small world
operties. The tagging behavior of users,
ich is explained by preferential attachment
the tags in social bookmark systems, is
flected in the distribution of single query
rds in search engines. We can conclude
at the clicking behaviour of search engine
ers based on the displayed search results
d the tagging behaviour of social bookmarking
ers is driven by similar dynamics.

@inproceedings{krause2008logsonomy, author = {Krause, Beate and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd}, title = {Logsonomy - Social Information Retrieval with Logdata}, booktitle = {HT '08: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia}, publisher = {ACM}, address = {New York, NY, USA}, year = {2008}, pages = {157--166}, url = {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}, doi = {http://doi.acm.org/10.1145/1379092.1379123}, isbn = {978-1-59593-985-2}, keywords = {2.0, 2008, analysis, folksonomy, information, itegpub, logsonomy, myown, network, retrieval, search, social, tagorapub, web, web2.0, web20}, abstract = {Social bookmarking systems constitute an established
rt of the Web 2.0. In such systems
ers describe bookmarks by keywords
lled tags. The structure behind these social
stems, called folksonomies, can be viewed
a tripartite hypergraph of user, tag and resource
des. This underlying network shows
ecific structural properties that explain its
owth and the possibility of serendipitous
ploration.
day’s search engines represent the gateway
retrieve information from the World Wide
b. Short queries typically consisting of
o to three words describe a user’s information
ed. In response to the displayed
sults of the search engine, users click on
e links of the result page as they expect
e answer to be of relevance.
is clickdata can be represented as a folksonomy
which queries are descriptions of
icked URLs. The resulting network structure,
ich we will term logsonomy is very
milar to the one of folksonomies. In order
find out about its properties, we analyze
e topological characteristics of the tripartite
pergraph of queries, users and bookmarks
a large snapshot of del.icio.us and
query logs of two large search engines.
l of the three datasets show small world
operties. The tagging behavior of users,
ich is explained by preferential attachment
the tags in social bookmark systems, is
flected in the distribution of single query
rds in search engines. We can conclude
at the clicking behaviour of search engine
ers based on the displayed search results
d the tagging behaviour of social bookmarking
ers is driven by similar dynamics.} }

%0 = inproceedings %A = Krause, Beate and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd %B = HT '08: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia %C = New York, NY, USA %D = 2008 %I = ACM %T = Logsonomy - Social Information Retrieval with Logdata %U = 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

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.

@inproceedings{1367620, author = {Leskovec, Jure and Horvitz, Eric}, title = {Planetary-scale views on a large instant-messaging network}, booktitle = {WWW '08: Proceeding of the 17th international conference on World Wide Web}, publisher = {ACM}, address = {New York, NY, USA}, year = {2008}, pages = {915--924}, url = {http://portal.acm.org/citation.cfm?id=1367620}, doi = {http://doi.acm.org/10.1145/1367497.1367620}, isbn = {978-1-60558-085-2}, keywords = {analysis, messenger, msn, network, seminar2009, sna, social}, abstract = {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.} }

%0 = inproceedings %A = Leskovec, Jure and Horvitz, Eric %B = WWW '08: Proceeding of the 17th international conference on World Wide Web %C = New York, NY, USA %D = 2008 %I = ACM %T = Planetary-scale views on a large instant-messaging network %U = http://portal.acm.org/citation.cfm?id=1367620

Abstract. Communities in social networks are often defined as groups of densely connected actors. However, members of the same dense group are not equal but may differ largely in their social position or in the role they play. Furthermore, the same positions can be found across the borders of dense communities so that networks contain a significant group
ructure which does not coincide with the structure of dense groups.
is papers gives a survey over formalizations of network-positions with a special emphasis on the use of algebraic notions.

@inproceedings{Brandes07Role, author = {Brandes, Ulrik and Lerner, Jürgen}, title = {Role-equivalent Actors in Networks}, editor = {Obiedkov, Sergei and Roth, Camille}, booktitle = {ICFCA 2007 Satellite Workshop on Social Network Analysis and Conceptual Structures: Exploring Opportunities}, year = {2007}, url = {http://www.inf.uni-konstanz.de/algo/publications/bl-rean-07.pdf}, keywords = {analysis, network, sna, social, structure}, abstract = {Abstract. Communities in social networks are often defined as groups of densely connected actors. However, members of the same dense group are not equal but may differ largely in their social position or in the role they play. Furthermore, the same positions can be found across the borders of dense communities so that networks contain a significant group
ructure which does not coincide with the structure of dense groups.
is papers gives a survey over formalizations of network-positions with a special emphasis on the use of algebraic notions.} }

%0 = inproceedings %A = Brandes, Ulrik and Lerner, Jürgen %B = ICFCA 2007 Satellite Workshop on Social Network Analysis and Conceptual Structures: Exploring Opportunities %D = 2007 %T = Role-equivalent Actors in Networks %U = http://www.inf.uni-konstanz.de/algo/publications/bl-rean-07.pdf

Literaturangaben

@book{Carrington:2007, author = {Carrington, Peter J.}, title = {Models and methods in social network analysis}, series = {Structural analysis in the social sciences}, publisher = {Cambridge Univ. Press Cambridge [u.a.]}, year = {2007}, url = {http://opac.bibliothek.uni-kassel.de/DB=23/PPN?PPN=190999837}, isbn = {0-521-80959-2}, keywords = {Mathematisches, Modell, Netzwerkanalyse, Soziologie, analysis, methods, models, network, sna, social}, abstract = {Literaturangaben} }

%0 = book %A = Carrington, Peter J. %D = 2007 %I = Cambridge Univ. Press Cambridge [u.a.] %T = Models and methods in social network analysis %U = http://opac.bibliothek.uni-kassel.de/DB=23/PPN?PPN=190999837

@proceedings{alani2006proceedings,, title = {Proceedings of the 2nd Workshop on Semantic Network Analysis}, editor = {Alani, Harith and Hoser, Bettina and Schmitz, Christoph and Stumme, Gerd}, year = {2006}, url = {http://www.kde.cs.uni-kassel.de/ws/sna2006/}, keywords = {2006, Network, Semantic, analysis, eswc, l3s, myown, nepomuk, network, proceedings, semantic, workshop} }

%0 = proceedings %B = } %C = %D = 2006 %I = %T = Proceedings of the 2nd Workshop on Semantic Network Analysis} %U = http://www.kde.cs.uni-kassel.de/ws/sna2006/

Social bookmark tools are rapidly emerging on the Web. In such
stems users are setting up lightweight conceptual structures
lled folksonomies. These systems provide currently relatively few
ructure. We discuss in this paper, how association rule mining
n be adopted to analyze and structure folksonomies, and how the results can be used
r ontology learning and supporting emergent semantics. We
monstrate our approach on a large scale dataset stemming from an
line system.

@inproceedings{schmitz2006mining, author = {Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd}, title = {Mining Association Rules in Folksonomies}, editor = {Batagelj, V. and Bock, H.-H. and Ferligoj, A. and Žiberna, A.}, booktitle = {Data Science and Classification. Proceedings of the 10th IFCS Conf.}, series = {Studies in Classification, Data Analysis, and Knowledge Organization}, publisher = {Springer}, address = {Heidelberg}, year = {2006}, pages = {261--270}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/schmitz2006mining.pdf}, keywords = {2006, analysis, fca, folksonomies, folksonomy, l3s, myown, nepomuk, network, semantic}, abstract = {Social bookmark tools are rapidly emerging on the Web. In such
stems users are setting up lightweight conceptual structures
lled folksonomies. These systems provide currently relatively few
ructure. We discuss in this paper, how association rule mining
n be adopted to analyze and structure folksonomies, and how the results can be used
r ontology learning and supporting emergent semantics. We
monstrate our approach on a large scale dataset stemming from an
line system.} }

%0 = inproceedings %A = Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd %B = Data Science and Classification. Proceedings of the 10th IFCS Conf. %C = Heidelberg %D = 2006 %I = Springer %T = Mining Association Rules in Folksonomies %U = http://www.kde.cs.uni-kassel.de/stumme/papers/2006/schmitz2006mining.pdf

Literaturverz. S. [193] - 204

@book{Scott:2005, author = {Scott, John}, title = {Social network analysis}, publisher = {Sage Publ. London [u.a.]}, year = {2005}, url = {http://opac.bibliothek.uni-kassel.de/DB=23/PPN?PPN=128222697}, isbn = {0-7619-6338-3}, keywords = {Netzwerkanalyse, Soziologie, analysis, network, sna, social}, abstract = {Literaturverz. S. [193] - 204} }

%0 = book %A = Scott, John %D = 2005 %I = Sage Publ. London [u.a.] %T = Social network analysis %U = http://opac.bibliothek.uni-kassel.de/DB=23/PPN?PPN=128222697

@proceedings{stumme05semanticnetworkanalysis,, title = {Proceedings of the First Workshop on Semantic Network Analysis }, editor = {Stumme, Gerd and Hoser, Bettina and Schmitz, Christoph and Alani, Harith}, publisher = {CEUR Proceedings}, address = {Aachen}, year = {2005}, url = {http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-171/}, issn = {1613-0073}, keywords = {2005, analysis, iswc, itegpub, l3s, myown, nepomuk, network, proceedings, semantic, semna, sna, workshop} }

%0 = proceedings %B = } %C = Aachen %D = 2005 %I = CEUR Proceedings %T = Proceedings of the First Workshop on Semantic Network Analysis } %U = http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-171/

@inproceedings{freeman03finding, author = {Freeman, Linton C.}, title = {Finding Social Groups: A Meta-Analysis of the Southern Women Data}, editor = {Breiger, Ronald and Carley, Kathleen and Pattison, Philippa}, booktitle = {Dynamic Social Network Modeling and Analysis: Workshop Summary and Papers}, publisher = {National Academies Press}, year = {2003}, keywords = {analysis, network, sna, social, southern, women} }

%0 = inproceedings %A = Freeman, Linton C. %B = Dynamic Social Network Modeling and Analysis: Workshop Summary and Papers %D = 2003 %I = National Academies Press %T = Finding Social Groups: A Meta-Analysis of the Southern Women Data