@inproceedings{krause2008logsonomy,
        title = {Logsonomy - Social Information Retrieval with Logdata},
        address = {New York, NY, USA},
        author = {Beate Krause and Robert Jäschke and Andreas Hotho and Gerd Stumme},
        booktitle = {HT '08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia},
        pages = {157--166},
        publisher = {ACM},
        year = 2008,
        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},
        location = {Pittsburgh, PA, USA},
	isbn = {978-1-59593-985-2},
	doi = {http://doi.acm.org/10.1145/1379092.1379123},
	abstract = {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.},
	biburl = {http://www.bibsonomy.org/bibtex/276d81124951ae39060a8bc98f4883435/jaeschke},
	keywords = {engine social retrieval information logsonomy analysis l3s wp5 search network for:nepomuk}
}

@inproceedings{ahn2007topological,
        title = {Analysis of topological characteristics of huge online social networking services},
        address = {New York, NY, USA},
        author = {Yong-Yeol Ahn and Seungyeop Han and Haewoon Kwak and Sue Moon and Hawoong Jeong},
        booktitle = {Proceedings of the 16th international conference on World Wide Web},
        pages = {835--844},
        publisher = {ACM},
        year = 2007,
        url = {http://portal.acm.org/citation.cfm?id=1242685},
        location = {Banff, Alberta, Canada},
	isbn = {978-1-59593-654-7},
	doi = {http://doi.acm.org/10.1145/1242572.1242685},
	description = {Analysis of topological characteristics of huge online social networking services},
	biburl = {http://www.bibsonomy.org/bibtex/2441b644b330fec7951c274a502c26e58/jaeschke},
	keywords = {online analysis social network folksonomy}
}

@misc{Ramos06,
        title = {On Self-Regulated Swarms, Societal Memory, Speed and Dynamics},
        author = {Vitorino Ramos and Carlos Fernandes and Agostinho C. Rosa},
        note = {arXiv:cs/0512002v1},
        year = 2006,
        url = {http://arxiv.org/abs/cs/0512002},
        id = {407750},
	priority = {4},
	conference = {International Conference on the Simulation and Synthesis of Living Systems},
	abstract = {Wasps, bees, ants and termites all make effective use of their environment and resources by displaying collective "swarm" intelligence. Termite colonies - for instance - build nests with a complexity far beyond the comprehension of the individual termite, while ant colonies dynamically allocate labor to various vital tasks such as foraging or defense without any central decision-making ability. Recent research suggests that microbial life can be even richer: highly social, intricately networked, and teeming with interactions, as found in bacteria. What strikes from these observations is that both ant colonies and bacteria have similar natural mechanisms based on Stigmergy and Self-Organization in order to emerge coherent and sophisticated patterns of global foraging behavior. Keeping in mind the above characteristics we propose a Self-Regulated Swarm (SRS) algorithm which hybridizes the advantageous characteristics of Swarm Intelligence as the emergence of a societal environmental memory or cognitive map via collective pheromone laying in the landscape (properly balancing the exploration/exploitation nature of our dynamic search strategy), with a simple Evolutionary mechanism that trough a direct reproduction procedure linked to local environmental features is able to self-regulate the above exploratory swarm population, speeding it up globally. In order to test his adaptive response and robustness, we have recurred to different dynamic multimodal complex functions as well as to Dynamic Optimization Control problems, measuring reaction speeds and performance. Final comparisons were made with standard Genetic Algorithms (GAs), Bacterial Foraging strategies (BFOA), as well as with recent Co-Evolutionary approaches. SRS's were able to demonstrate quick adaptive responses, while outperforming the results obtained by the other approaches. Additionally, some successful behaviors were found: SRS was able to maintain a number of different solutions, while adapting to unforeseen situations even when over the same cooperative foraging period, the community is requested to deal with two different and contradictory purposes; the possibility to spontaneously create and maintain different subpopulations on different peaks, emerging different exploratory corridors with intelligent path planning capabilities; the ability to request for new agents (division of labor) over dramatic changing periods, and economizing those foraging resources over periods of intermediate stabilization. Finally, results illustrate that the present SRS collective swarm of bio-inspired ant-like agents is able to track about 65% of moving peaks traveling up to ten times faster than the velocity of a single individual composing that precise swarm tracking system. This emerged behavior is probably one of the most interesting ones achieved by the present work.},
	biburl = {http://www.bibsonomy.org/bibtex/2804ad41798bd794f4f85c96bd6217127/jaeschke},
	keywords = {swarm network trias_example iccs_example cognition social}
}

@techreport{Ramos05,
        title = {Social Cognitive Maps, Swarm Collective Perception and Distributed Search on Dynamic Landscapes},
        author = {Vitorino Ramos and Carlos Fernandes and Agostinho C. Rosa},
        institution = {Insituto Superior Técnico, Universidade Técnica de Lisboa},
        note = {arXiv:nlin/0502057v1},
        number = {CVRM-IST 127E-2005},
        year = 2005,
        url = {http://arxiv.org/abs/nlin/0502057},
        id = {407689},
	priority = {4},
	abstract = {Swarm Intelligence (SI) is the property of a system whereby the collective behaviors of (unsophisticated) entities interacting locally with their environment cause coherent functional global patterns to emerge. SI provides a basis with wich it is possible to explore collective (or distributed) problem solving without centralized control or the provision of a global model. To tackle the formation of a coherent social collective intelligence from individual behaviors, we discuss several concepts related to Self-Organization, Stigmergy and Social Foraging in animals. Then, in a more abstract level we suggest and stress the role played not only by the environmental media as a driving force for societal learning, as well as by positive and negative feedbacks produced by the many interactions among agents. Finally, presenting a simple model based on the above features, we will adress the collective adaptation of a social community to a cultural (environmenatl, contextual) or media informational dynamical landscape, represented here - for the purpose of different experiments - by several three-dimensional mathematical functions that suddenly change over time. Results indicate that the collective intelligence is able to cope and quickly adapt to unforseen situations even when over the same cooperative foraging period, the community is requested to deal with two different and contradictory purposes.},
	biburl = {http://www.bibsonomy.org/bibtex/27debdcf93027a77b3b928caae4121dff/jaeschke},
	keywords = {perception cognition trias_example network iccs_example social swarm}
}

@inproceedings{jaeschke2007analysis,
        title = {Analysis of the Publication Sharing Behaviour in {BibSonomy}},
        address = {Berlin, Heidelberg},
        author = {Robert Jäschke and Andreas Hotho and Christoph Schmitz and Gerd Stumme},
        booktitle = {Proceedings of the 15th International Conference on Conceptual Structures (ICCS 2007)},
        editor = {U. Priss and S. Polovina and R. Hill},
        month = {July},
        pages = {283--295},
        publisher = {Springer-Verlag},
        series = {Lecture Notes in Artificial Intelligence},
        volume = 4604,
        year = 2007,
        isbn = {3-540-73680-8},
	vgwort = {22},
	abstract = {BibSonomy is a web-based social resource sharing system which allows users to organise and share bookmarks and publications in a collaborative manner. In this paper we present the system, followed by a description of the insights in the structure of its bibliographic data that we gained by applying techniques we developed in the area of Formal Concept Analysis.},
	biburl = {http://www.bibsonomy.org/bibtex/20c2b212b9ea3d822bf4729fd5fe6b6e1/jaeschke},
	keywords = {2007 bibsonomy analysis iccs fca myown l3s folksonomy social trias bookmarking}
}

@misc{candia-2007,
        title = {Uncovering individual and collective human dynamics from mobile phone records},
        author = {J. Candia and M. C. Gonzalez and P. Wang and T. Schoenharl and G. Madey and A. L. Barabasi},
        year = 2007,
        url = {http://www.citebase.org/abstract?id=oai:arXiv.org:0710.2939},
        abstract = { Novel aspects of human dynamics and social interactions are investigated by means of mobile phone data. Using extensive phone records resolved in both time and space, we study the mean collective behavior at large scales and focus on the occurrence of anomalous events. We discuss how these spatiotemporal anomalies can be described using standard percolation theory tools. We also investigate patterns of calling activity at the individual level and show that the interevent time of consecutive calls is heavy-tailed. This finding, which has implications for dynamics of spreading phenomena in social networks, agrees with results previously reported on other human activities.},
	biburl = {http://www.bibsonomy.org/bibtex/2ea8b6a4442ccc0cb7dd222f6bd1d992a/jaeschke},
	keywords = {dynamic analysis social network mobile phone sna}
}

@inproceedings{conf/ht/WuZM06,
        title = {Harvesting social knowledge from folksonomies.},
        author = {Harris Wu and Mohammad Zubair and Kurt Maly},
        booktitle = {Hypertext},
        crossref = {conf/ht/2006},
        editor = {Uffe Kock Wiil and Peter J. Nürnberg and Jessica Rubart},
        pages = {111-114},
        publisher = {ACM},
        year = 2006,
        url = {http://dblp.uni-trier.de/db/conf/ht/ht2006.html#WuZM06},
        ee = {http://doi.acm.org/10.1145/1149941.1149962},
	isbn = {1-59593-417-0},
	date = {2006-09-28},
	description = {dblp},
	biburl = {http://www.bibsonomy.org/bibtex/24b0512091911843390f88699d3ea3bb9/jaeschke},
	keywords = {folksonomy knowledge social}
}

@unpublished{Regulski07Aufwand,
        title = {Aufwand und Nutzen beim Einsatz von Social Bookmarking Services als Nachweisinstrument für wissenschaftliche Forschungsartikel am Beispiel von BibSonomy},
        author = {Katharina Regulski},
        year = 2007,
        url = {http://www.bibliothek-saur.de/preprint/2007/ar2460_regulski.pdf},
        abstract = {Authors of scientific article have numerous options to search for background material for their research projects.
With our article, we want to show that the use of Social-Bookmarking-Services as part of the web 2.0
(O’Reilly, 2005)/library 2.0 (Danowski, 2006) technology is a useful supplement to conventional reference
databases.
},
	biburl = {http://www.bibsonomy.org/bibtex/22e40f8be9de6920e49f86ec960b7ccc7/jaeschke},
	keywords = {tagging social study folksonomy bibsonomy}
}

@inproceedings{Brandes07Role,
        title = {Role-equivalent Actors in Networks},
        author = {Ulrik Brandes and Jürgen Lerner},
        booktitle = {ICFCA 2007 Satellite Workshop on Social Network Analysis and Conceptual Structures: Exploring Opportunities},
        editor = {Sergei Obiedkov and Camille Roth},
        year = 2007,
        url = {http://www.inf.uni-konstanz.de/algo/publications/bl-rean-07.pdf},
        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
structure which does not coincide with the structure of dense groups.
This papers gives a survey over formalizations of network-positions with a special emphasis on the use of algebraic notions.},
	biburl = {http://www.bibsonomy.org/bibtex/26ea541158f972b850e9ea330b473c7c4/jaeschke},
	keywords = {structure sna network role analysis actor social}
}

@inproceedings{Jason_Euzenat_2007,
        title = {Towards Semantic Social Networks},
        address = {Berlin Heidelberg, Germany},
        author = {Jason Jung and Jérôme Euzenat},
        booktitle = {Proceedings of the European Semantic Web Conference (ESWC2007)},
        editor = {Enrico Franconi and Michael Kifer and Wolfgang May},
        month = {July},
        pages = {267-280},
        publisher = {Springer-Verlag},
        series = {LNCS},
        volume = 4519,
        year = 2007,
        url = {http://www.eswc2007.org/pdf/eswc07-jung.pdf},
        biburl = {http://www.bibsonomy.org/bibtex/2d628e5621e97187b71d54bf3c2700670/jaeschke},
	keywords = {sna eswc semantic network social 2007}
}

@inproceedings{hjss06bibsonomy,
        title = {{BibSonomy}: A Social Bookmark and Publication Sharing System},
        address = {Aalborg, Denmark},
        author = {Andreas Hotho and Robert Jäschke and Christoph Schmitz and Gerd Stumme},
        booktitle = {Proceedings of the Conceptual Structures Tool Interoperability Workshop at the 14th International Conference on Conceptual Structures},
        editor = {Aldo de Moor and Simon Polovina and Harry Delugach},
        month = {July},
        publisher = {Aalborg University Press},
        year = 2006,
        url = {http://www.kde.cs.uni-kassel.de/jaeschke/paper/hotho06bibsonomy.pdf},
        isbn = {87-7307-769-0},
	vgwort = {27},
	biburl = {http://www.bibsonomy.org/bibtex/22cbd8e3236adea7c54779605a5aa4fd6/jaeschke},
	keywords = {social iccs_example 2006 bookmarking iccs l3s folksonomy trias_example myown bibsonomy}
}

@inproceedings{1244292,
        title = {FCA-based approach for mining contextualized folksonomy},
        address = {New York, NY, USA},
        author = {Hak Lae Kim and Suk Hyung Hwang and Hong Gee Kim},
        booktitle = {SAC '07: Proceedings of the 2007 ACM symposium on Applied computing},
        pages = {1340--1345},
        publisher = {ACM Press},
        year = 2007,
        url = {http://portal.acm.org/citation.cfm?id=1244002.1244292&coll=GUIDE&dl=},
        location = {Seoul, Korea},
	isbn = {1-59593-480-4},
	doi = {http://doi.acm.org/10.1145/1244002.1244292},
	biburl = {http://www.bibsonomy.org/bibtex/24440c3ca148004f3759456eac34e84fa/jaeschke},
	keywords = {concept folksonomy network social mining formal tagging analysis fca}
}

@article{lhfh05social,
        title = {{S}ocial {B}ookmarking {T}ools ({II}): {A} {C}ase {S}tudy - {C}onnotea},
        author = {Ben Lund and Tony Hammond and Martin Flack and Timo Hannay},
        journal = {D-Lib Magazine},
        month = {April},
        number = 4,
        organization = {{N}ature {P}ublishing {G}roup},
        volume = 11,
        year = 2005,
        biburl = {http://www.bibsonomy.org/bibtex/213958ef5da2d2133b9b84e9a3cb40da1/jaeschke},
	keywords = {iccs_example tool trias_example folksonomy social bookmarking tagging}
}

@article{hhls05social,
        title = {{S}ocial {B}ookmarking {T}ools ({I}): {A} {G}eneral {R}eview},
        author = {Tony Hammond and Timo Hannay and Ben Lund and Joanna Scott},
        journal = {D-Lib Magazine},
        month = {April},
        number = 4,
        organization = {{N}ature {P}ublishing {G}roup},
        volume = 11,
        year = 2005,
        biburl = {http://www.bibsonomy.org/bibtex/289c6c43ad692ccfbe4c09d31926ab8a7/jaeschke},
	keywords = {trias_example tool iccs_example folksonomy social bookmarking tagging}
}

@book{wasserman_faust_94,
        title = {Social Network Analysis: Methods and Applications},
        author = {Stanley Wasserman and Katherine Faust and Dawn Iacobucci and Mark Granovetter},
        publisher = {Cambridge University Press},
        year = 1994,
        biburl = {http://www.bibsonomy.org/bibtex/276dc2c519de8e00a07fcb27aeda6e472/jaeschke},
	keywords = {analysis network sna social}
}

@techreport{citeulike:739394,
        title = {Collaborative Creation of Communal Hierarchical Taxonomies in Social Tagging Systems},
        author = {Paul Heymann and Hector Garcia-Molina},
        institution = {Computer Science Department},
        month = {April},
        number = {2006-10},
        school = {Standford University},
        year = 2006,
        url = {http://dbpubs.stanford.edu:8090/pub/2006-10},
        id = {739394},
	priority = {3},
	abstract = {Collaborative tagging systems---systems where many casual users annotate objects with free-form strings (tags) of their choosing---have recently emerged as a powerful way to label and organize large collections of data. During our recent investigation into these types of systems, we discovered a simple but remarkably effective algorithm for converting a large corpus of tags annotating objects in a tagging system into a navigable hierarchical taxonomy of tags. We first discuss the algorithm and then present a preliminary model to explain why it is so effective in these types of systems.},
	biburl = {http://www.bibsonomy.org/bibtex/23b4ce6fd7fa6dbf1c39fd261fa39fcd6/jaeschke},
	keywords = {taxonomy collaborative social tagging folksonomy}
}

@phdthesis{trier05visualization,
        title = {IT-supported Visualization and Evaluation of Virtual Knowledge Communities. Applying Social Network Intelligence Software in Knowledge Management to enable knowledge oriented People Network Management},
        author = {Matthias Trier},
        year = 2005,
        url = {http://nbn-resolving.de/urn/resolver.pl?urn=urn:nbn:de:kobv:83-opus-10720},
        biburl = {http://www.bibsonomy.org/bibtex/266eb70a04e6946077182446170dd6dcf/jaeschke},
	keywords = {community network detection social management knowledge}
}

@inproceedings{citeulike:391307,
        title = {The author-topic model for authors and documents},
        address = {Arlington, VA, USA},
        author = {Michal Rosen-Zvi and Thomas Griffiths and Mark Steyvers and Padhraic Smyth},
        booktitle = {Proceedings of the 20th conference on Uncertainty in artificial intelligence},
        pages = {487--494},
        publisher = {AUAI Press},
        year = 2004,
        url = {http://portal.acm.org/citation.cfm?id=1036843.1036902},
        id = {391307},
	priority = {0},
	isbn = {0974903906},
	biburl = {http://www.bibsonomy.org/bibtex/2a4dd688efe5778fb99ff94de104211aa/jaeschke},
	keywords = {topicinference network socialnets social community}
}

@article{newman03fast,
        title = {Fast algorithm for detecting community structure in networks},
        author = {M.E.J. Newman},
        journal = {Physical Review E},
        month = {September},
        volume = 69,
        year = 2003,
        url = {http://arxiv.org/abs/cond-mat/0309508},
        biburl = {http://www.bibsonomy.org/bibtex/256de7e6d214faebdbf2f2ef0fce09d7d/jaeschke},
	keywords = {community social network clustering gn algorithm}
}

@article{gn02community,
        title = {Community structure in social and biological networks},
        author = {Michelle Girvan and M.E.J. Newman},
        journal = {Proceedings of the National Academy of Science},
        number = 12,
        pages = {7821-7826},
        volume = 99,
        year = 2002,
        abstract = {A number of recent studies have focused on the statistical properties of networked systems such as social networks and the Worldwide Web. Researchers have concentrated particularly on a few properties that seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this article, we highlight another property that is found in many networks, the property of community structure, in which network nodes are joined together in tightly knit groups, between which there are only looser connections. We propose a method for detecting such communities, built around the idea of using centrality indices to find community boundaries. We test our method on computer-generated and real-world graphs whose community structure is already known and find that the method detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well known---a collaboration network and a food web---and find that it detects significant and informative community divisions in both cases.},
	biburl = {http://www.bibsonomy.org/bibtex/28f80a8586927ea69ea915b6c32e87629/jaeschke},
	keywords = {network gn social detection structure community}
}