BibSonomy publications for /user/jaeschke/communitySat Jul 12 13:03:47 CEST 2008WebKDD 2008 Workshop on Web Mining and Web Usage AnalysisAugustTo AppearDetecting Commmunities via Simultaneous Clustering of Graphs and Folksonomies2008detection clustering community Fri May 18 10:42:10 CEST 2007Physical Review E027104Community detection in complex networks using Extremal Optimization722005network complex community detection We propose a novel method to find the community structure in complex networks based on an extremal optimization of the value of modularity. The method outperforms the optimal modularity found by the existing algorithms in the literature. We present the results of the algorithm for computer simulated and real networks and compare them with other approaches. The efficiency and accuracy of the method make it feasible to be used for the accurate identification of community structure in large complex networks.Wed May 16 09:18:29 CEST 2007Frankfurt am MainPhD Thesis (2002)39Europäische HochschulschriftenUnterstützung der Formierung und Analyse von virtuellen Communities412003toread analysis community support Systeme, die den Informationsaustausch in Communities unterstützen, sind heute allgegenwärtig. Eine zielgerichtete Analyse solcher Communities ist allerdings nur schwer möglich, denn es gibt bislang kein Verfahren zur formalen Beschreibung virtueller Communities, auf dem aufbauend eine Analyse stattfinden könnte. Es wird ein Konzept vorgestellt, das die Brücke schlägt zwischen den natürlichsprachlichen Beschreibungen von virtuellen Communities in der Soziologie und der Psychologie, und einer formalen Beschreibung, wie sie für die zielgerichtete Software-Entwicklung nötig ist. Neben einem formalen Modell von virtuellen Communities wird ein komponentenbasierter Ansatz vorgestellt, der beschreibt, wie mit diesem Modell gezielt Unterstützungs- und Analysesysteme entwickelt werden können.Wed May 16 09:08:27 CEST 2007April99-04Community Space: Toward Flexible Support for Voluntary Knowledge Communities1999community knowledge In this paper we describe CommunitySpace, a component of a project to support voluntary, electronic communities of practice. We detail some of our design decisions, emphasizing issues of flexibility, diversity, and democracy. These design decisions will have impact upon the user interface to CommunitySpace, but they have much more immediate impact upon the architecture, representation, and dynamics of usage of the system. Our work is in the requirements and design phase, and we are interested in the comments of our peers on our evolving ideas.Wed May 16 09:03:13 CEST 2007Ulvik, Hardanger Fjord, Norway24th annual Information Systems Research Seminar in ScandinaviaAugust2001Communities of Interest: Learning through the Interaction of Multiple Knowledge Systems2001practice learning interest community knowledge Wed May 16 08:58:56 CEST 2007MarchOrtsbezug in kontext-sensitiven Diensten für mobile Communities8. Münchner Fortbildungsseminar Geoinformationssysteme2003mobile sensitive context community geo service location Thu Feb 01 14:04:37 CET 2007Halle-WittenbergProc. 18. Workshop Grundlagen von DatenbankenJune80-84Wege zur Entdeckung von Communities in Folksonomies2006community 2006 trias_example iccs_example folksonomy detection l3s myown Ein wichtiger Baustein des neu entdeckten World Wide Web -- des "Web 2.0" -- stellen Folksonomies dar. In diesen Systemen können Benutzer gemeinsam Ressourcen verwalten und mit Schlagwörtern versehen. Die dadurch entstehenden begrifflichen Strukturen stellen ein interessantes Forschungsfeld dar. Dieser Artikel untersucht Ansätze und Wege zur Entdeckung und Strukturierung von Nutzergruppen ("Communities") in Folksonomies.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 Management2005social network management community knowledge detection 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 community socialnets network topicinference Tue May 16 12:18:19 CEST 2006SDMOnline Analysis of Community Evolution in Data Streams.2005stream detection analysis community data Tue May 16 12:15:36 CEST 2006Proceedings of the 4th International Conference on Internet Computing17--23Design and evaluation of a user-based community discovery technique2003hits detection network community Tue May 16 12:12:26 CEST 2006New York, NY, USAProceedings of the 13th international conference on World Wide Web413--421A community-aware search engine2004community detection engine network search hits Current search technologies work in a "one size fits all" fashion. Therefore, the answer to a query is independent of specific user information need. In this paper we describe a novel ranking technique for personalized search servicesthat combines content-based and community-based evidences. The community-based information is used in order to provide context for queries andis influenced by the current interaction of the user with the service. Ouralgorithm is evaluated using data derived from an actual service available on the Web an online bookstore. We show that the quality of content-based ranking strategies can be improved by the use of communityinformation as another evidential source of relevance. In our experiments the improvements reach up to 48% in terms of average precision.Tue May 16 12:09:13 CEST 2006New York, NY, USAProceedings of the 10th international conference on World Wide Web415--429Finding authorities and hubs from link structures on the World Wide Web2001network detection community hits Tue May 16 08:38:35 CEST 2006Proceedings of the Eighteenth National Conference on Artificial IntelligenceConference ProceedingsJuly798--804Stochastic Link and Group Detection2002network community gda detection Tue May 16 08:37:04 CEST 2006Pittsburgh, PASeptemberCMU-RI-TR-03-32K-groups: Tractable Group Detection on Large Link Data Sets2003large community gda network detection Discovering underlying structure from co-occurrence data is an important task in many fields, including: insurance, intelligence, criminal investigation, epidemiology, human resources, and marketing. For example a store may wish to identify underlying sets of items purchased together or a human resources department may wish to identify groups of employees that collaborate with each other. Previously Kubica et. al. presented the group detection algorithm (GDA) - an algorithm for finding underlying groupings of entities from co-occurrence data. This algorithm is based on a probabilistic generative model and produces coherent groups that are consistent with prior knowledge. Unfortunately, the optimization used in GDA is slow, making it potentially infeasible for many real world data sets. To this end, we present k-groups - an algorithm that uses an approach similar to that of k-means (hard clustering and localized updates) to significantly accelerate the discovery of the underlying groups while retaining GDA's probabilistic model. In addition, we show that k-groups is guaranteed to converge to a local minimum. We also compare the performance of GDA and k-groups on several real world and artificial data sets, showing that k-groups' sacrifice in solution quality is significantly offset by its increase in speed. This trade-off makes group detection tractable on significantly larger data sets.Tue May 16 08:33:46 CEST 2006The Third IEEE International Conference on Data MiningNovember573-576Tractable Group Detection on Large Link Data Sets2003network large gda community detection Mon May 15 17:09:39 CEST 2006FebDefining and identifying communities in networks2004gn detection network community graph The investigation of community structures in networks is an important issue in many domains and disciplines. This problem is relevant for social tasks (objective analysis of relationships on the web), biological inquiries (functional studies in metabolic, cellular or protein networks) or technological problems (optimization of large infrastructures). Several types of algorithm exist for revealing the community structure in networks, but a general and quantitative definition of community is still lacking, leading to an intrinsic difficulty in the interpretation of the results of the algorithms without any additional non-topological information. In this paper we face this problem by introducing two quantitative definitions of community and by showing how they are implemented in practice in the existing algorithms. In this way the algorithms for the identification of the community structure become fully self-contained. Furthermore, we propose a new local algorithm to detect communities which outperforms the existing algorithms with respect to the computational cost, keeping the same level of reliability. The new algorithm is tested on artificial and real-world graphs. In particular we show the application of the new algorithm to a network of scientific collaborations, which, for its size, can not be attacked with the usual methods. This new class of local algorithms could open the way to applications to large-scale technological and biological applications.Mon May 15 16:45:35 CEST 2006Physical Review ESeptemberFast algorithm for detecting community structure in networks692003community social gn algorithm clustering network Mon May 15 16:42:47 CEST 2006Physical Review E066111Finding community structure in very large networks702004gn structure large detection community network newman Mon May 15 16:42:39 CEST 2006Physical Review E026113Finding and evaluating community structure in networks692004network structure girvan detection community newman gn