http://www.bibsonomy.org/burst/user/jaeschke/socialBibSonomy BuRST Feed for /user/jaeschke/social2008-07-21T01:10:20+02:00http://www.bibsonomy.org/bibtex/276d81124951ae39060a8bc98f4883435/jaeschkejaeschke2008-06-20T14:42:46+02:00information analysis for:nepomuk engine social l3s logsonomy retrieval network wp5 search <span style="color:#555555;">Beate <a href="http://www.bibsonomy.org/author/Krause">Krause</a> and Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>HT '08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia, </em><em>page157--166. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2008</em>)Fri Jun 20 14:42:46 CEST 2008New York, NY, USAHT '08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia157--166Logsonomy - Social Information Retrieval with Logdata2008information analysis for:nepomuk engine social l3s logsonomy retrieval network wp5 search 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.Analysis of topological characteristics of huge online social networking serviceshttp://www.bibsonomy.org/bibtex/2441b644b330fec7951c274a502c26e58/jaeschkejaeschke2008-05-20T17:48:35+02:00online social network analysis folksonomy <span style="color:#555555;">Yong-Yeol <a href="http://www.bibsonomy.org/author/Ahn">Ahn</a> and Seungyeop <a href="http://www.bibsonomy.org/author/Han">Han</a> and Haewoon <a href="http://www.bibsonomy.org/author/Kwak">Kwak</a> and Sue <a href="http://www.bibsonomy.org/author/Moon">Moon</a> and Hawoong <a href="http://www.bibsonomy.org/author/Jeong">Jeong</a> </span><em>Proceedings of the 16th international conference on World Wide Web, </em><em>page835--844. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2007</em>)Tue May 20 17:48:35 CEST 2008New York, NY, USAProceedings of the 16th international conference on World Wide Web835--844Analysis of topological characteristics of huge online social networking services2007online social network analysis folksonomy http://www.bibsonomy.org/bibtex/2804ad41798bd794f4f85c96bd6217127/jaeschkejaeschke2008-03-03T13:28:01+01:00cognition swarm iccs_example trias_example social network <span style="color:#555555;">Vitorino <a href="http://www.bibsonomy.org/author/Ramos">Ramos</a> and Carlos <a href="http://www.bibsonomy.org/author/Fernandes">Fernandes</a> and Agostinho C. <a href="http://www.bibsonomy.org/author/Rosa">Rosa</a> </span>(<em>2006</em>) <em>arXiv:cs/0512002v1 . </em>Mon Mar 03 13:28:01 CET 2008arXiv:cs/0512002v1On Self-Regulated Swarms, Societal Memory, Speed and Dynamics2006cognition swarm iccs_example trias_example social network 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.http://www.bibsonomy.org/bibtex/27debdcf93027a77b3b928caae4121dff/jaeschkejaeschke2008-03-03T12:05:49+01:00social perception iccs_example network cognition trias_example swarm <span style="color:#555555;">Vitorino <a href="http://www.bibsonomy.org/author/Ramos">Ramos</a> and Carlos <a href="http://www.bibsonomy.org/author/Fernandes">Fernandes</a> and Agostinho C. <a href="http://www.bibsonomy.org/author/Rosa">Rosa</a> </span><em>CVRM-IST 127E-2005. </em><em>Insituto Superior T&#233;cnico, Universidade T&#233;cnica de Lisboa, </em>(<em>2005</em>)Mon Mar 03 12:05:49 CET 2008arXiv:nlin/0502057v1CVRM-IST 127E-2005Social Cognitive Maps, Swarm Collective Perception and Distributed Search on Dynamic Landscapes2005social perception iccs_example network cognition trias_example swarm 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.http://www.bibsonomy.org/bibtex/20c2b212b9ea3d822bf4729fd5fe6b6e1/jaeschkejaeschke2008-01-17T13:49:53+01:00bibsonomy folksonomy 2007 trias iccs analysis bookmarking fca myown social l3s <span style="color:#555555;">Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Christoph <a href="http://www.bibsonomy.org/author/Schmitz">Schmitz</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Proceedings of the 15th International Conference on Conceptual Structures (ICCS 2007), </em><em>volume4604ofLecture Notes in Artificial Intelligence, </em><em>page283--295. </em><em>Berlin, Heidelberg, </em><em>Springer-Verlag, </em><em>July2007. </em>Thu Jan 17 13:49:53 CET 2008Berlin, HeidelbergProceedings of the 15th International Conference on Conceptual Structures (ICCS 2007)July283--295Lecture Notes in Artificial IntelligenceAnalysis of the Publication Sharing Behaviour in {BibSonomy}46042007bibsonomy folksonomy 2007 trias iccs analysis bookmarking fca myown social l3s 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.http://www.bibsonomy.org/bibtex/2ea8b6a4442ccc0cb7dd222f6bd1d992a/jaeschkejaeschke2008-01-15T13:11:57+01:00phone network social analysis sna dynamic mobile <span style="color:#555555;">J. <a href="http://www.bibsonomy.org/author/Candia">Candia</a> and M. C. <a href="http://www.bibsonomy.org/author/Gonzalez">Gonzalez</a> and P. <a href="http://www.bibsonomy.org/author/Wang">Wang</a> and T. <a href="http://www.bibsonomy.org/author/Schoenharl">Schoenharl</a> and G. <a href="http://www.bibsonomy.org/author/Madey">Madey</a> and A. L. <a href="http://www.bibsonomy.org/author/Barabasi">Barabasi</a> </span>(<em>2007</em>)Tue Jan 15 13:11:57 CET 2008Uncovering individual and collective human dynamics from mobile phone records2007phone network social analysis sna dynamic mobile 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.dblphttp://www.bibsonomy.org/bibtex/24b0512091911843390f88699d3ea3bb9/jaeschkejaeschke2007-08-09T08:03:23+02:00knowledge folksonomy social <span style="color:#555555;">Harris <a href="http://www.bibsonomy.org/author/Wu">Wu</a> and Mohammad <a href="http://www.bibsonomy.org/author/Zubair">Zubair</a> and Kurt <a href="http://www.bibsonomy.org/author/Maly">Maly</a> </span><em>Hypertext, </em><em>page111-114. </em><em>ACM, </em>(<em>2006</em>)Thu Aug 09 08:03:23 CEST 2007Hypertextconf/ht/2006111-114Harvesting social knowledge from folksonomies.2006knowledge folksonomy social http://www.bibsonomy.org/bibtex/22e40f8be9de6920e49f86ec960b7ccc7/jaeschkejaeschke2007-08-08T16:23:05+02:00social folksonomy bibsonomy tagging study <span style="color:#555555;">Katharina <a href="http://www.bibsonomy.org/author/Regulski">Regulski</a> </span>(<em>2007</em>)Wed Aug 08 16:23:05 CEST 2007Aufwand und Nutzen beim Einsatz von Social Bookmarking Services als Nachweisinstrument für wissenschaftliche Forschungsartikel am Beispiel von BibSonomy2007social folksonomy bibsonomy tagging study 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. http://www.bibsonomy.org/bibtex/26ea541158f972b850e9ea330b473c7c4/jaeschkejaeschke2007-08-08T14:26:54+02:00network analysis actor social structure sna role <span style="color:#555555;">Ulrik <a href="http://www.bibsonomy.org/author/Brandes">Brandes</a> and J&#252;rgen <a href="http://www.bibsonomy.org/author/Lerner">Lerner</a> </span><em>ICFCA 2007 Satellite Workshop on Social Network Analysis and Conceptual Structures: Exploring Opportunities, </em>(<em>2007</em>)Wed Aug 08 14:26:54 CEST 2007ICFCA 2007 Satellite Workshop on Social Network Analysis and Conceptual Structures: Exploring OpportunitiesRole-equivalent Actors in Networks2007network analysis actor social structure sna role 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.http://www.bibsonomy.org/bibtex/2d628e5621e97187b71d54bf3c2700670/jaeschkejaeschke2007-07-16T12:40:02+02:00sna social semantic 2007 network eswc <span style="color:#555555;">Jason <a href="http://www.bibsonomy.org/author/Jung">Jung</a> and J&#233;r&#244;me <a href="http://www.bibsonomy.org/author/Euzenat">Euzenat</a> </span><em>Proceedings of the European Semantic Web Conference (ESWC2007), </em><em>volume4519ofLNCS, </em><em>page267-280. </em><em>Berlin Heidelberg, Germany, </em><em>Springer-Verlag, </em><em>July2007. </em>Mon Jul 16 12:40:02 CEST 2007Berlin Heidelberg, GermanyProceedings of the European Semantic Web Conference (ESWC2007)July267-280LNCSTowards Semantic Social Networks45192007sna social semantic 2007 network eswc http://www.bibsonomy.org/bibtex/22cbd8e3236adea7c54779605a5aa4fd6/jaeschkejaeschke2007-07-10T16:17:49+02:00bookmarking 2006 iccs trias_example folksonomy myown bibsonomy l3s social iccs_example <span style="color:#555555;">Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> and Christoph <a href="http://www.bibsonomy.org/author/Schmitz">Schmitz</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Proceedings of the Conceptual Structures Tool Interoperability Workshop at the 14th International Conference on Conceptual Structures, </em><em>Aalborg, Denmark, </em><em>Aalborg University Press, </em><em>July2006. </em>Tue Jul 10 16:17:49 CEST 2007Aalborg, DenmarkProceedings of the Conceptual Structures Tool Interoperability Workshop at the 14th International Conference on Conceptual StructuresJuly{BibSonomy}: A Social Bookmark and Publication Sharing System2006bookmarking 2006 iccs trias_example folksonomy myown bibsonomy l3s social iccs_example http://www.bibsonomy.org/bibtex/24440c3ca148004f3759456eac34e84fa/jaeschkejaeschke2007-07-05T14:56:08+02:00mining social folksonomy analysis concept tagging fca network formal <span style="color:#555555;">Hak Lae <a href="http://www.bibsonomy.org/author/Kim">Kim</a> and Suk Hyung <a href="http://www.bibsonomy.org/author/Hwang">Hwang</a> and Hong Gee <a href="http://www.bibsonomy.org/author/Kim">Kim</a> </span><em>SAC '07: Proceedings of the 2007 ACM symposium on Applied computing, </em><em>page1340--1345. </em><em>New York, NY, USA, </em><em>ACM Press, </em>(<em>2007</em>)Thu Jul 05 14:56:08 CEST 2007New York, NY, USASAC '07: Proceedings of the 2007 ACM symposium on Applied computing1340--1345FCA-based approach for mining contextualized folksonomy2007mining social folksonomy analysis concept tagging fca network formal http://www.bibsonomy.org/bibtex/213958ef5da2d2133b9b84e9a3cb40da1/jaeschkejaeschke2007-02-01T14:04:37+01:00social iccs_example tool tagging folksonomy bookmarking trias_example <span style="color:#555555;">Ben <a href="http://www.bibsonomy.org/author/Lund">Lund</a> and Tony <a href="http://www.bibsonomy.org/author/Hammond">Hammond</a> and Martin <a href="http://www.bibsonomy.org/author/Flack">Flack</a> and Timo <a href="http://www.bibsonomy.org/author/Hannay">Hannay</a> </span><em>D-Lib Magazine</em><em>April2005. </em>Thu Feb 01 14:04:37 CET 2007D-Lib MagazineApril4{S}ocial {B}ookmarking {T}ools ({II}): {A} {C}ase {S}tudy - {C}onnotea112005social iccs_example tool tagging folksonomy bookmarking trias_example http://www.bibsonomy.org/bibtex/289c6c43ad692ccfbe4c09d31926ab8a7/jaeschkejaeschke2007-02-01T14:04:37+01:00tool social iccs_example bookmarking folksonomy tagging trias_example <span style="color:#555555;">Tony <a href="http://www.bibsonomy.org/author/Hammond">Hammond</a> and Timo <a href="http://www.bibsonomy.org/author/Hannay">Hannay</a> and Ben <a href="http://www.bibsonomy.org/author/Lund">Lund</a> and Joanna <a href="http://www.bibsonomy.org/author/Scott">Scott</a> </span><em>D-Lib Magazine</em><em>April2005. </em>Thu Feb 01 14:04:37 CET 2007D-Lib MagazineApril4{S}ocial {B}ookmarking {T}ools ({I}): {A} {G}eneral {R}eview112005tool social iccs_example bookmarking folksonomy tagging trias_example http://www.bibsonomy.org/bibtex/276dc2c519de8e00a07fcb27aeda6e472/jaeschkejaeschke2006-12-05T09:28:06+01:00analysis sna network social <span style="color:#555555;">Stanley <a href="http://www.bibsonomy.org/author/Wasserman">Wasserman</a> and Katherine <a href="http://www.bibsonomy.org/author/Faust">Faust</a> and Dawn <a href="http://www.bibsonomy.org/author/Iacobucci">Iacobucci</a> and Mark <a href="http://www.bibsonomy.org/author/Granovetter">Granovetter</a> </span><em>Cambridge University Press, </em>(<em>1994</em>)Tue Dec 05 09:28:06 CET 2006Social Network Analysis: Methods and Applications1994analysis sna network social http://www.bibsonomy.org/bibtex/23b4ce6fd7fa6dbf1c39fd261fa39fcd6/jaeschkejaeschke2006-10-24T10:04:16+02:00tagging collaborative folksonomy social taxonomy <span style="color:#555555;">Paul <a href="http://www.bibsonomy.org/author/Heymann">Heymann</a> and Hector <a href="http://www.bibsonomy.org/author/Garcia-Molina">Garcia-Molina</a> </span><em>2006-10. </em><em>Computer Science Department, </em><em>April2006. </em>Tue Oct 24 10:04:16 CEST 2006April2006-10Collaborative Creation of Communal Hierarchical Taxonomies in Social Tagging Systems2006tagging collaborative folksonomy social taxonomy 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.http://www.bibsonomy.org/bibtex/266eb70a04e6946077182446170dd6dcf/jaeschkejaeschke2006-09-14T08:49:50+02:00management detection knowledge network social community <span style="color:#555555;">Matthias <a href="http://www.bibsonomy.org/author/Trier">Trier</a> </span>(<em>2005</em>)Thu Sep 14 08:49:50 CEST 2006IT-supported Visualization and Evaluation of Virtual Knowledge Communities. Applying Social Network Intelligence Software in Knowledge Management to enable knowledge oriented People Network Management2005management detection knowledge network social community http://www.bibsonomy.org/bibtex/2a4dd688efe5778fb99ff94de104211aa/jaeschkejaeschke2006-07-27T11:36:09+02:00social network socialnets community topicinference <span style="color:#555555;">Michal <a href="http://www.bibsonomy.org/author/Rosen-Zvi">Rosen-Zvi</a> and Thomas <a href="http://www.bibsonomy.org/author/Griffiths">Griffiths</a> and Mark <a href="http://www.bibsonomy.org/author/Steyvers">Steyvers</a> and Padhraic <a href="http://www.bibsonomy.org/author/Smyth">Smyth</a> </span><em>Proceedings of the 20th conference on Uncertainty in artificial intelligence, </em><em>page487--494. </em><em>Arlington, VA, USA, </em><em>AUAI Press, </em>(<em>2004</em>)Thu Jul 27 11:36:09 CEST 2006Arlington, VA, USAProceedings of the 20th conference on Uncertainty in artificial intelligence487--494The author-topic model for authors and documents2004social network socialnets community topicinference http://www.bibsonomy.org/bibtex/256de7e6d214faebdbf2f2ef0fce09d7d/jaeschkejaeschke2006-05-15T16:45:35+02:00network community clustering gn social algorithm <span style="color:#555555;">M.E.J. <a href="http://www.bibsonomy.org/author/Newman">Newman</a> </span><em>Physical Review E</em><em>September2003. </em>Mon May 15 16:45:35 CEST 2006Physical Review ESeptemberFast algorithm for detecting community structure in networks692003network community clustering gn social algorithm http://www.bibsonomy.org/bibtex/28f80a8586927ea69ea915b6c32e87629/jaeschkejaeschke2006-05-15T16:30:32+02:00detection gn structure network social community <span style="color:#555555;">Michelle <a href="http://www.bibsonomy.org/author/Girvan">Girvan</a> and M.E.J. <a href="http://www.bibsonomy.org/author/Newman">Newman</a> </span><em>Proceedings of the National Academy of Science</em><em>99(12):7821-7826</em>(<em>2002</em>)Mon May 15 16:30:32 CEST 2006Proceedings of the National Academy of Science127821-7826Community structure in social and biological networks992002detection gn structure network social community 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.