@inproceedings{Backstrom:2007:WAT:1242572.1242598,
  abstract = {In a social network, nodes correspond topeople or other social entities, and edges correspond to social links between them. In an effort to preserve privacy, the practice of anonymization replaces names with meaningless unique identifiers. We describe a family of attacks such that even from a single anonymized copy of a social network, it is possible for an adversary to learn whether edges exist or not between specific targeted pairs of nodes.},
  acmid = {1242598},
  address = {New York, NY, USA},
  author = {Backstrom, Lars and Dwork, Cynthia and Kleinberg, Jon},
  booktitle = {Proceedings of the 16th international conference on World Wide Web},
  doi = {10.1145/1242572.1242598},
  interhash = {aa7d0f96c372d2c03d228f27a7f4b66b},
  intrahash = {913059fcbf0453c60ff8b79e2705742c},
  isbn = {978-1-59593-654-7},
  location = {Banff, Alberta, Canada},
  numpages = {10},
  pages = {181--190},
  publisher = {ACM},
  series = {WWW '07},
  title = {Wherefore art thou r3579x?: anonymized social networks, hidden patterns, and structural steganography},
  url = {http://doi.acm.org/10.1145/1242572.1242598},
  year = 2007
}

@inproceedings{mueller-2012,
  abstract = {The connection of ubiquitous and social computing is an emerging research
 area which is combining two prominent areas of computer science.
 In this paper, we tackle this topic from different angles: We describe
 data mining methods for ubiquitous and social data, specifically
 focusing on physical and social activities, and provide exemplary
 analysis results. Furthermore, we give an overview on the Ubicon
 platform which provides a framework for the creation and hosting
 of ubiquitous and social applications for diverse tasks and projects.
 Ubicon features the collection and analysis of both physical and
 social activities of users for enabling inter-connected applications
 in ubiquitous and social contexts. We summarize three real-world
 systems built on top of Ubicon, and exemplarily discuss the according
 mining and analysis aspects.},
  address = {Washington, DC, USA},
  author = {Atzmueller, Martin and Becker, Martin and Doerfel, Stephan and Kibanov, Mark and Hotho, Andreas and Macek, Björn-Elmar and Mitzlaff, Folke and Mueller, Juergen and Scholz, Christoph and Stumme, Gerd},
  booktitle = {IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2012, Besançon, France, 20-23 November, 2012},
  interhash = {a2695fd9fe6e76b252edbd42d72b34ad},
  intrahash = {90847b1d969ac1ed1f4c8d7146416619},
  publisher = {IEEE},
  title = {Ubicon: Observing Social and Physical Activities},
  year = 2012
}

@article{goldbergtracking,
  author = {Goldberg, Mark and Magdon ismail, Malik and Nambirajan, Srinivas and Thompson, James},
  interhash = {cfde2673d41ea69c4ec2750749f76c8e},
  intrahash = {17a06b71460472ba0212a03debbe6a42},
  title = {Tracking and Predicting Evolution of Social Communities},
  url = {http://130.203.133.150/viewdoc/summary;jsessionid=F5AEFEF80D05D6B5A0262BA46AEE899D?doi=10.1.1.225.2523},
  year = {?}
}

@inproceedings{koerner2010thinking,
  abstract = {Recent research provides evidence for the presence of emergent semantics in collaborative tagging systems. While several methods have been proposed, little is known about the factors that influence the evolution of semantic structures in these systems. A natural hypothesis is that the quality of the emergent semantics depends on the pragmatics of tagging: Users with certain usage patterns might contribute more to the resulting semantics than others. In this work, we propose several measures which enable a pragmatic differentiation of taggers by their degree of contribution to emerging semantic structures. We distinguish between categorizers, who typically use a small set of tags as a replacement for hierarchical classification schemes, and describers, who are annotating resources with a wealth of freely associated, descriptive keywords. To study our hypothesis, we apply semantic similarity measures to 64 different partitions of a real-world and large-scale folksonomy containing different ratios of categorizers and describers. Our results not only show that ‘verbose’ taggers are most useful for the emergence  of tag semantics, but also that a subset containing only 40% of the most ‘verbose’ taggers can produce results that match and even outperform the semantic precision obtained from the whole dataset. Moreover, the results suggest that there exists a causal link between the pragmatics of tagging and resulting emergent semantics. This work is relevant for designers and analysts of tagging systems interested (i) in fostering the semantic development of their platforms, (ii) in identifying users introducing “semantic noise”, and (iii) in learning ontologies.},
  address = {Raleigh, NC, USA},
  author = {Körner, Christian and Benz, Dominik and Strohmaier, Markus and Hotho, Andreas and Stumme, Gerd},
  booktitle = {Proceedings of the 19th International World Wide Web Conference (WWW 2010)},
  interhash = {5afe6e4ce8357d8ac9698060fb438468},
  intrahash = {45f8d8f2a8251a5e988c596a5ebb3f2d},
  month = apr,
  publisher = {ACM},
  title = {Stop Thinking, start Tagging - Tag Semantics emerge from Collaborative Verbosity},
  url = {http://www.kde.cs.uni-kassel.de/benz/papers/2010/koerner2010thinking.pdf},
  year = 2010
}

@incollection{marinho2011social,
  abstract = {The new generation of Web applications known as (STS) is successfully established and poised for continued growth. STS are open and inherently social; features that have been proven to encourage participation. But while STS bring new opportunities, they revive old problems, such as information overload. Recommender Systems are well known applications for increasing the level of relevant content over the  noise  that continuously grows as more and more content becomes available online. In STS however, we face new challenges. Users are interested in finding not only content, but also tags and even other users. Moreover, while traditional recommender systems usually operate over 2-way data arrays, STS data is represented as a third-order tensor or a hypergraph with hyperedges denoting (user, resource, tag) triples. In this chapter, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve STS.We describe (a) novel facets of recommenders for STS, such as user, resource, and tag recommenders, (b) new approaches and algorithms for dealing with the ternary nature of STS data, and (c) recommender systems deployed in real world STS. Moreover, a concise comparison between existing works is presented, through which we identify and point out new research directions.},
  address = {New York},
  author = {Balby Marinho, Leandro and Nanopoulos, Alexandros and Schmidt-Thieme, Lars and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd and Symeonidis, Panagiotis},
  booktitle = {Recommender Systems Handbook},
  doi = {10.1007/978-0-387-85820-3_19},
  editor = {Ricci, Francesco and Rokach, Lior and Shapira, Bracha and Kantor, Paul B.},
  interhash = {2d4afa6f7fb103ccc166c9c5d629cdd1},
  intrahash = {708be7b5c269bd3a9d3d2334f858d52d},
  isbn = {978-0-387-85820-3},
  pages = {615--644},
  publisher = {Springer},
  title = {Social Tagging Recommender Systems},
  url = {http://dx.doi.org/10.1007/978-0-387-85820-3_19},
  year = 2011
}

@book{balbymarinho2012recommender,
  abstract = {Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.},
  author = {Balby Marinho, L. and Hotho, A. and Jäschke, R. and Nanopoulos, A. and Rendle, S. and Schmidt-Thieme, L. and Stumme, G. and Symeonidis, P.},
  doi = {10.1007/978-1-4614-1894-8},
  interhash = {0bb7f0588cd690d67cc73e219a3a24fa},
  intrahash = {87d6883ebd98e8810be45d7e7e4ade96},
  isbn = {978-1-4614-1893-1},
  month = feb,
  publisher = {Springer},
  series = {SpringerBriefs in Electrical and Computer Engineering},
  title = {Recommender Systems for Social Tagging Systems},
  url = {http://link.springer.com/book/10.1007/978-1-4614-1894-8},
  year = 2012
}

@article{palla2007quantifying,
  author = {Palla, Gergely and Barabási, Albert-lászló and Vicsek, Tamás and Hungary, Budapest},
  interhash = {911359764769215213b2d31dc6394078},
  intrahash = {9c5a50822cb5580b66a7371b833bb2fe},
  pages = 2007,
  title = {Quantifying social group evolution},
  url = {http://130.203.133.150/viewdoc/summary?doi=10.1.1.119.7541},
  volume = 446,
  year = 2007
}

@inproceedings{bullock2011privacyaware,
  abstract = {With the increased popularity of Web 2.0 services in the last years data privacy has become a major concern for users. The more personal data users reveal, the more difficult it becomes to control its disclosure in the web. However, for Web 2.0 service providers, the data provided by users is a valuable source for offering effective, personalised data mining services. One major application is the detection of spam in social bookmarking systems: in order to prevent a decrease of content quality, providers need to distinguish spammers and exclude them from the system. They thereby experience a conflict of interests: on the one hand, they need to identify spammers based on the information they collect about users, on the other hand, they need to respect privacy concerns and process as few personal data as possible. It would therefore be of tremendous help for system developers and users to know which personal data are needed for spam detection and which can be ignored. In this paper we address these questions by presenting a data privacy aware feature engineering approach. It consists of the design of features for spam classification which are evaluated according to both, performance and privacy conditions. Experiments using data from the social bookmarking system BibSonomy show that both conditions must not exclude each other.},
  acmid = {2024306},
  address = {New York, NY, USA},
  articleno = {15},
  author = {Bullock, Beate Navarro and Lerch, Hana and Ro\ssnagel, Alexander and Hotho, Andreas and Stumme, Gerd},
  booktitle = {Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies},
  doi = {10.1145/2024288.2024306},
  interhash = {7a2d6a35c124ea0fe31c962f8f150916},
  intrahash = {00a8f31185a34957eb16d500d7d51398},
  isbn = {978-1-4503-0732-1},
  location = {Graz, Austria},
  numpages = {8},
  pages = {15:1--15:8},
  publisher = {ACM},
  series = {i-KNOW '11},
  title = {Privacy-aware spam detection in social bookmarking systems},
  url = {http://doi.acm.org/10.1145/2024288.2024306},
  year = 2011
}

@inproceedings{benz2011measuring,
  abstract = {Recent research has demonstrated how the widespread adoption of collaborative tagging systems yields emergent semantics. In recent years, much has been learned about how to harvest the data produced by taggers for engineering light-weight ontologies. For example, existing measures of tag similarity and tag relatedness have proven crucial step stones for making latent semantic relations in tagging systems explicit. However, little progress has been made on other issues, such as understanding the different levels of tag generality (or tag abstractness), which is essential for, among others, identifying hierarchical relationships between concepts. In this paper we aim to address this gap. Starting from a review of linguistic definitions of word abstractness, we first use several large-scale ontologies and taxonomies as grounded measures of word generality, including Yago, Wordnet, DMOZ and Wikitaxonomy. Then, we introduce and apply several folksonomy-based methods to measure the level of generality of given tags. We evaluate these methods by comparing them with the grounded measures. Our results suggest that the generality of tags in social tagging systems can be approximated with simple measures. Our work has implications for a number of problems related to social tagging systems, including search, tag recommendation, and the acquisition of light-weight ontologies from tagging data.},
  address = {Heraklion, Crete},
  author = {Benz, Dominik and Körner, Christian and Hotho, Andreas and Stumme, Gerd and Strohmaier, Markus},
  booktitle = {Proceedings of the 8th Extended Semantic Web Conference (ESWC 2011)},
  editor = {Antoniou, Grigoris and Grobelnik, Marko and Simperl, Elena and Parsia, Bijan and Plexousakis, Dimitris and Pan, Jeff and Leenheer, Pieter De},
  interhash = {33a2078f3836293d71c449d5376fc440},
  intrahash = {b245d492f1f9fa41b62b79b6dec77241},
  month = may,
  title = {One Tag to Bind Them All: Measuring Term Abstractness in Social Metadata},
  url = {http://www.kde.cs.uni-kassel.de/pub/pdf/benz2011measuring.pdf},
  year = 2011
}

@inproceedings{benz2011measuring,
  author = {Benz, Dominik and Körner, Christian and Hotho, Andreas and Stumme, Gerd and Strohmaier, Markus},
  booktitle = {Working Notes of the LWA 2011 - Learning, Knowledge, Adaptation},
  interhash = {33a2078f3836293d71c449d5376fc440},
  intrahash = {923d369285422c758398cbe92e3532cd},
  title = {One Tag to Bind Them All: Measuring Term Abstractness in Social Metadata},
  year = 2011
}

@inproceedings{SAS:12,
  address = {Boston, MA, USA},
  author = {Scholz, Christoph and Atzmueller, Martin and Stumme, Gerd},
  booktitle = {Proc. Fourth ASE/IEEE International Conference on Social Computing (SocialCom)},
  interhash = {9bc5d42018dbe8b926be214190258b3c},
  intrahash = {be5ae4b92170e7c595f5fdcac15b4786},
  publisher = {IEEE Computer Society},
  title = {{On the Predictability of Human Contacts: Influence Factors and the Strength of Stronger Ties}},
  url = {http://www.kde.cs.uni-kassel.de/atzmueller/paper/scholz-on-f2f-predictability-socialcom-2012.pdf},
  year = 2012
}

@book{ACHH:12,
  address = {Heidelberg, Germany},
  editor = {Atzmueller, Martin and Chin, Alvin and Helic, Denis and Hotho, Andreas},
  interhash = {ebf8e8b66c6c0723092e11e40998d61f},
  intrahash = {a0e5d144b39199fa4acb6319f29e7a15},
  publisher = {Springer Verlag},
  series = {Lecture Notes in Computer Science},
  title = {Modeling and Mining Ubiquitous Social Media},
  url = {http://www.springer.com/computer/ai/book/978-3-642-33683-6},
  volume = 7472,
  year = 2012
}

@article{Atzmueller:12c,
  author = {Atzmueller, Martin},
  interhash = {0b20c1d53d5df05326d594726273c2fb},
  intrahash = {7b616e64994893a2aad95b5ad95db662},
  journal = {WIREs: Data Mining and Knowledge Discovery},
  title = {{Mining Social Media: Key Players, Sentiments, and Communities}},
  volume = {In Press},
  year = 2012
}

@inproceedings{hristova2012mapping,
  abstract = {Communities of people are better mappers if they are spatially
clustered, as revealed in an interesting new paper by Hristova,
Mashhadi, Quattrone and Capra from UCL. "This preliminary analysis
inspires further inquiry because it shows a clear correlation between
spatial affiliation, the internal community structure and the
community’s engagement in terms of coverage", according to the
authors. They have studied the similarity patterns among eight hundred
contributors to OpenStreetMap, the well-known crowdmapping project and
detected the hidden community structure. It is a very promising field
of research, coupling a social network analysis of crowdsourced data.
Participants to such projects are rarely independent individuals: in
most cases, they involve communities more than single participants and
it would be crucial to uncover how the underlying social structure
reflects on the quantity and the quality of the collected data. It has
the greatest relevance for citizen science projects, as data quality
is often the key issue determining the success or the failure of the
collective effort. },
  author = {Hristova, Desislava and Mashhadi, Afra and Quattrone, Giovanni and Capra, Licia},
  booktitle = {Proc. When the City Meets the Citizen Workshop (WCMCW)},
  interhash = {373e02fe56d30b26261a33135e0b7a45},
  intrahash = {f0a69ac56b94a471b470ebd56545fafd},
  month = jun,
  title = {Mapping Community Engagement with Urban Crowd-Sourcing},
  url = {http://www.cs.ucl.ac.uk/staff/l.capra/publications/wcmcw12.pdf},
  year = 2012
}

@inproceedings{doerfel2012leveraging,
  abstract = {The ever-growing flood of new scientific articles requires novel retrieval mechanisms. One means for mitigating this instance of the information overload phenomenon are collaborative tagging systems, that allow users to select, share and annotate references to publications. These systems employ recommendation algorithms to present to their users personalized lists of interesting and relevant publications. In this paper we analyze different ways to incorporate social data and metadata from collaborative tagging systems into the graph-based ranking algorithm FolkRank to utilize it for recommending scientific articles to users of the social bookmarking system BibSonomy. We compare the results to those of Collaborative Filtering, which has previously been applied for resource recommendation.},
  address = {New York, NY, USA},
  author = {Doerfel, Stephan and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd},
  booktitle = {Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web},
  doi = {10.1145/2365934.2365937},
  interhash = {beb2c81daf975eeed6e01e1b412196b1},
  intrahash = {64bf590675a833770b7d284871435a8d},
  isbn = {978-1-4503-1638-5},
  location = {Dublin, Ireland},
  month = sep,
  pages = {9--16},
  publisher = {ACM},
  title = {Leveraging Publication Metadata and Social Data into FolkRank for Scientific Publication Recommendation },
  url = {http://doi.acm.org/10.1145/2365934.2365937},
  year = 2012
}

@inproceedings{bozsak2002towards,
  author = {Bozsak, E. and Ehrig, Marc and Handschuh, Siegfried and Hotho, Andreas and Maedche, Alexander and Motik, Boris and Oberle, Daniel and Schmitz, Christoph and Staab, Steffen and Stojanovic, Ljiljana and Stojanovic, Nenad and Studer, Rudi and Stumme, Gerd and Sure, York and Tane, Julien and Volz, Raphael and Zacharias, Valentin},
  booktitle = {Proceedings of the Third International Conference on E-Commerce and Web Technologies (EC-Web 2002), Aix-en-Provence, France},
  editor = {Bauknecht, Kurt and Tjoa, A. Min and Quirchmayr, Gerald},
  interhash = {940750309ac472ea48a712e16b5d902a},
  intrahash = {d0aa1d2d01e378046e1693babc026836},
  pages = {304-313},
  publisher = {Springer},
  series = {LNCS},
  title = {KAON - Towards a large scale Semantic Web},
  url = {http://www.aifb.uni-karlsruhe.de/WBS/ysu/publications/2002_ecweb_kaon.pdf},
  volume = 2455,
  year = 2002
}

@inproceedings{BAKHSS:11,
  address = {Chemnitz},
  author = {Behrenbruch, Kay and Atzmueller, Martin and Kniewel, Romy and Hoberg, Sebastian and Stumme, Gerd and Schmidt, Ludger},
  booktitle = {GfA-Frühjahrskongress},
  interhash = {bb1435b451f54abf143ea892375abf55},
  intrahash = {addbaaba7aec8360e23284c849e216ad},
  title = {Gestaltung technisch-sozialer Vernetzung in der Arbeitsorganisation: Untersuchung zur Nutzerakzeptanz von RFID-Technologie},
  year = 2011
}

@incollection{ADHMS:12,
  address = {Heidelberg, Germany},
  alteditor = {Editor},
  author = {Atzmueller, Martin and Doerfel, Stephan and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd},
  booktitle = {{Modeling and Mining Ubiquitous Social Media}},
  interhash = {4f1f4b515b01cc448a91b3e368deabad},
  intrahash = {d81d6f6ccdf3ff6572898d39c90e6354},
  publisher = {Springer Verlag},
  series = {LNAI},
  title = {Face-to-Face Contacts at a Conference: Dynamics of Communities and Roles},
  url = {http://www.kde.cs.uni-kassel.de/atzmueller/paper/atzmueller-face-to-face-contacts-dynamics-lnai-2012.pdf},
  volume = 7472,
  year = 2012
}

@misc{Narayanan2009,
  abstract = {  Operators of online social networks are increasingly sharing potentially
sensitive information about users and their relationships with advertisers,
application developers, and data-mining researchers. Privacy is typically
protected by anonymization, i.e., removing names, addresses, etc.
  We present a framework for analyzing privacy and anonymity in social networks
and develop a new re-identification algorithm targeting anonymized
social-network graphs. To demonstrate its effectiveness on real-world networks,
we show that a third of the users who can be verified to have accounts on both
Twitter, a popular microblogging service, and Flickr, an online photo-sharing
site, can be re-identified in the anonymous Twitter graph with only a 12% error
rate.
  Our de-anonymization algorithm is based purely on the network topology, does
not require creation of a large number of dummy "sybil" nodes, is robust to
noise and all existing defenses, and works even when the overlap between the
target network and the adversary's auxiliary information is small.
},
  author = {Narayanan, Arvind and Shmatikov, Vitaly},
  interhash = {ffb21f5ed2b9b879d911d0b68f3d5c07},
  intrahash = {396299d0adaba60baa0f4c2bd28a93b8},
  note = {cite arxiv:0903.3276
Comment: Published in the 30th IEEE Symposium on Security and Privacy, 2009.
  The definitive version is available at:
  http://www.cs.utexas.edu/~shmat/shmat_oak09.pdf Frequently Asked Questions
  are answered at: http://www.cs.utexas.edu/~shmat/socialnetworks-faq.html},
  title = {De-anonymizing Social Networks},
  url = {http://arxiv.org/abs/0903.3276},
  year = 2009
}

@incollection{jaeschke2012challenges,
  abstract = {Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.},
  address = {Berlin/Heidelberg},
  affiliation = {Knowledge & Data Engineering Group, University of Kassel, Wilhelmshöher Allee 73, 34121 Kassel, Germany},
  author = {Jäschke, Robert and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd},
  booktitle = {Recommender Systems for the Social Web},
  doi = {10.1007/978-3-642-25694-3_3},
  editor = {Pazos Arias, José J. and Fernández Vilas, Ana and Díaz Redondo, Rebeca P.},
  interhash = {75b1a6f54ef54d0126d0616b5bf77563},
  intrahash = {7d41d332cccc3e7ba8e7dadfb7996337},
  isbn = {978-3-642-25694-3},
  pages = {65--87},
  publisher = {Springer},
  series = {Intelligent Systems Reference Library},
  title = {Challenges in Tag Recommendations for Collaborative Tagging Systems},
  url = {http://dx.doi.org/10.1007/978-3-642-25694-3_3},
  volume = 32,
  year = 2012
}