BibSonomy publications for /tag/socialnetsSat Jan 05 23:30:04 CET 2008OnlineAprilOPUS - IT-supported Visualization and Evaluation of Virtual Knowledge Communities. Applying Social Network Intelligence Software in Knowledge Management to enable knowledge oriented People Network Management2005socialnets D2.2, community, Visualization, WP2, After analyzing current economic indicators to prove the necessity of Knowledge Management for maintaining competitiveness, the basic foundations of the discipline are analyzed. This includes a detailed discussion of the core term �knowledge� itself as well as a comprehensive overview of the development of KM approaches. This results in a timeline which shows the development of the research discipline of KM during the past decades. Afterwards, the underlying complex theories of systems sciences and sociology are developed towards an overview about properties and requirements of modern and complex network organizations. As a result, in part 3.6., novel and concrete implications for a modern and network oriented approach to KM are derived from this discussion. Subsequently, Communities of Practice are identified and described as a major recent concept, which is an actual instantiation of a networked organization for organizing Knowledge Work in expert groups. Their main properties, roles, and processes of a community and its development through lifecycle stages are described. The resulting picture of the basic mechanisms is then extended with an extensive discussion of Information Technology to support the expert networks. However, the analysis results in the insight, that current IT is not at all satisfying the requirements of virtual Knowledge Communities in corporate applications. Especially, the important role of the community moderator and manager is unsatisfactorily supported. This person needs transparency about the large group he is responsible for. This implies the necessity of instruments for monitoring, measurement and evaluation, which is also emphasized by thought leaders and major institutions in the CoP area. Further, the sociability of the expert group needs to be improved. To address these issues, a comprehensive measurement system for analyzing virtual Knowledge Communities is developed. It draws its measures primarily from sociological domains, such as Social Capital and Trust research and Social Network Analysis; but it also includes Knowledge Processes and plain structural analysis. To implement the conceptualized support for CoPs with appropriate measures and visualizations, an extensive software solution which aids as an add-on to current community platforms has been developed and is introduced. The primary challenge was to create insightful visualizations, which integrate 2D and 3D Graph Drawing Techniques for Social Network Analysis with Topic and Keyword Analysis methods and to merge this conglomerate with the measurement system. Finally, three case studies are introduced to illustrate the application of the software solution and its benefits for providing a CoP moderator or manager with detailed insights about the structure and processes of his group.Sat Jan 05 23:30:02 CET 2008New York, NY, USACHI '06: CHI '06 extended abstracts on Human factors in computing systems1779--1782Making sense of social networks2006socialnets WP2, community, Visualization, D2.2, Mon Nov 05 17:28:31 CET 2007Arlington, VA, USAProceedings of the 20th conference on Uncertainty in artificial intelligence487--494The author-topic model for authors and documents2004topicinference socialnets Mon Nov 05 17:28:31 CET 2007KDD '03: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining266--275Algorithms for estimating relative importance in networks2003socialnets Sat Nov 03 00:18:07 CET 2007Banff Park Lodge, Banff, Canada20th Conference on Uncertainty in Artificial IntelligenceJulyThe Author-Topic Model for Authors and Documents212004socialnets topicinference We introduce the author-topic model, a gen- erative model for documents that extends La- tent Dirichlet Allocation (LDA; Blei, Ng, \& Jordan, 2003) to include authorship informa- tion. Each author is associated with a multi- nomial distribution over topics and each topic is associated with a multinomial distribution over words. A document with multiple au- thors is modeled as a distribution over topics that is a mixture of the distributions associ- ated with the authors. We apply the model to a collection of 1,700 NIPS conference pa- pers and 160,000 CiteSeer abstracts. Exact inference is intractable for these datasets and we use Gibbs sampling to estimate the topic and author distributions. We compare the performance with two other generative mod- els for documents, which are special cases of the author-topic model: LDA (a topic model) and a simple author model in which each au- thor is associated with a distribution over words rather than a distribution over top- ics. We show topics recovered by the author- topic model, and demonstrate applications to computing similarity between authors and entropy of author output.Sat Nov 03 00:18:07 CET 2007New York, NY, USAKDD '04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining306--315Probabilistic author-topic models for information discovery2004topicinference socialnets We propose a new unsupervised learning technique for extracting information from large text collections. We model documents as if they were generated by a two-stage stochastic process. Each author is represented by a probability distribution over topics, and each topic is represented as a probability distribution over words for that topic. The words in a multi-author paper are assumed to be the result of a mixture of each authors' topic mixture. The topic-word and author-topic distributions are learned from data in an unsupervised manner using a Markov chain Monte Carlo algorithm. We apply the methodology to a large corpus of 160,000 abstracts and 85,000 authors from the well-known CiteSeer digital library, and learn a model with 300 topics. We discuss in detail the interpretation of the results discovered by the system including specific topic and author models, ranking of authors by topic and topics by author, significant trends in the computer science literature between 1990 and 2002, parsing of abstracts by topics and authors and detection of unusual papers by specific authors. An online query interface to the model is also discussed that allows interactive exploration of author-topic models for corpora such as CiteSeer.Sat Aug 18 13:22:24 CEST 2007AugThe Structure of Collaborative Tagging Systems2005social collaboration social-networks networking eni community self-organization no-tag scale-free tag semantics collaborative_tagging farsi-media network_analysis sociology tagging socialtagging clustering socialbookmarking folksonomies semiotics delicious software trends emergent folksonomy ict computer-networks datamining classification cooperation networks information_organization socialnets socialsoftware web20 interface tags linguistics core_periphery collaborative collective wasabee emergence social_networks Collaborative tagging describes the process by which many users add metadata in the form of keywords to shared content. Recently, collaborative tagging has grown in popularity on the web, on sites that allow users to tag bookmarks, photographs and other content. In this paper we analyze the structure of collaborative tagging systems as well as their dynamical aspects. Specifically, we discovered regularities in user activity, tag frequencies, kinds of tags used, bursts of popularity in bookmarking and a remarkable stability in the relative proportions of tags within a given url. We also present a dynamical model of collaborative tagging that predicts these stable patterns and relates them to imitation and shared knowledge.Tue Apr 17 15:32:58 CEST 2007AugThe Structure of Collaborative Tagging Systems2005retrieve citation social-network farsi-media _unfiled network_analysis folksonomies quantitative research ecology asc archive software emergent commentary clustering collaborative shelaev-project eni social-networks export dynamic datamining socialsearch social networking no-tag overview community indexing golder information_organization cscw self-organization bookmark categorisation sigcr2006 networkdynamics social-space 519 collective folksonomy cooperation socialnetwork social-networking emergence patterns model search collaboration leei core_periphery ict annotation socialtagging social-netowrks ewa socialsoftware semiotics metadata digital colaborative collaborate p2p citations collaborative_tagging delicious socialnets social_software bookmarks bibliography linguistics learning socialnetworking social_networks social-tagging computer-networks references social-bookmarking structure sociology coordination history collaborative_filtering networks 0-student-shelaev semantics scale-free ontology properties bookmarking interface collaborative-filtering classification collaborative-tagging newmedia library20 socialbookmarking imt595 huberman dr_kim Collaborative tagging describes the process by which many users add metadata in the form of keywords to shared content. Recently, collaborative tagging has grown in popularity on the web, on sites that allow users to tag bookmarks, photographs and other content. In this paper we analyze the structure of collaborative tagging systems as well as their dynamical aspects. Specifically, we discovered regularities in user activity, tag frequencies, kinds of tags used, bursts of popularity in bookmarking and a remarkable stability in the relative proportions of tags within a given url. We also present a dynamical model of collaborative tagging that predicts these stable patterns and relates them to imitation and shared knowledge.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 documents2004network social socialnets community topicinference Fri Jun 16 10:34:37 CEST 2006New York, NY, USACommun. ACMMarch363--65Referral Web: combining social networks and collaborative filtering401997cscw community socialnets NUMEROUS STUDIES HAVE SHOWN THAT ONE OF THE MOST EFFECtive channels for disseminating of information and expertise within an organization is its informal network of collaborators, colleagues, and friends [1, 4, 7]. Indeed, the social network1 is as least as important as the official organizational structure for tasks ranging from immediate, local problem-solving (for example, fixing a piece of equipment), to primary work functions, such as creating project teams.Fri Jun 16 10:34:37 CEST 2006AprilEvolution of the social network of scientific collaborations2001science socialnets skillinference The co-authorship network of scientists represents a prototype of complex evolving networks. By mapping the electronic database containing all relevant journals in mathematics and neuro-science for an eight-year period (1991-98), we infer the dynamic and the structural mechanisms that govern the evolution and topology of this complex system. First, empirical measurements allow us to uncover the topological measures that characterize the network at a given moment, as well as the time evolution of these quantities. The results indicate that the network is scale-free, and that the network evolution is governed by preferential attachment, affecting both internal and external links. However, in contrast with most model predictions the average degree increases in time, and the node separation decreases. Second, we propose a simple model that captures the network's time evolution. Third, numerical simulations are used to uncover the behavior of quantities that could not be predicted analytically.Fri Jun 16 10:34:37 CEST 2006New York, NY, USACHI '03: Proceedings of the SIGCHI conference on Human factors in computing systems593--600Recommending collaboration with social networks: a comparative evaluation2003socialnets cscw Fri Jun 16 10:34:37 CEST 2006Proceedings of the 2000 ACM conference on Computer supported cooperative work231--240Expertise recommender: a flexible recommendation system and architecture2000cscw socialnets skillinference Fri Jun 16 10:34:37 CEST 2006New York, NY, USAKDD '04: Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining118--127Fast discovery of connection subgraphs2004skillinference community findingcommunities clustering socialnets We define a connection subgraph as a small subgraph of a large graph that best captures the relationship between two nodes. The primary motivation for this work is to provide a paradigm for exploration and knowledge discovery in large social networks graphs. We present a formal definition of this problem, and an ideal solution based on electricity analogues. We then show how to accelerate the computations, to produce approximate, but high-quality connection subgraphs in real time on very large (disk resident)graphs. We describe our operational prototype, and we demonstrate results on a social network graph derived from the World Wide Web. Our graph contains 15 million nodes and 96 million edges, and our system still produces quality responses within seconds.Fri Jun 16 10:34:37 CEST 2006NovemberThe Simultaneous Evolution of Author and Paper Networks2003skillinference socialnets science There has been a long history of research into the structure and evolution of mankind's scientific endeavor. However, recent progress in applying the tools of science to understand science itself has been unprecedented because only recently has there been access to high-volume and high-quality data sets of scientific output (e.g., publications, patents, grants), as well as computers and algorithms capable of handling this enormous stream of data. This paper reviews major work on models that aim to capture and recreate the structure and dynamics of scientific evolution. We then introduce a general process model that simultaneously grows co-author and paper-citation networks. The statistical and dynamic properties of the networks generated by this model are validated against a 20-year data set of articles published in the Proceedings of the National Academy of Science. Systematic deviations from a power law distribution of citations to papers are well fit by a model that incorporates a partitioning of authors and papers into topics, a bias for authors to cite recent papers, and a tendency for authors to cite papers cited by papers that they have read. In this TARL model (for Topics, Aging, and Recursive Linking), the number of topics is linearly related to the clustering coefficient of the simulated paper citation network.Fri Jun 16 10:34:37 CEST 2006SepHierarchical Organization in Complex Networks2002socialnets Many real networks in nature and society share two generic properties: they are scale-free and they display a high degree of clustering. We show that these two features are the consequence of a hierarchical organization, implying that small groups of nodes organize in a hierarchical manner into increasingly large groups, while maintaining a scale-free topology. In hierarchical networks the degree of clustering characterizing the different groups follows a strict scaling law, which can be used to identify the presence of a hierarchical organization in real networks. We find that several real networks, such as the World Wide Web, actor network, the Internet at the domain level and the semantic web obey this scaling law, indicating that hierarchy is a fundamental characteristic of many complex systems.Fri Jun 16 10:34:37 CEST 2006NovWho is the best connected scientist? A study of scientific coauthorship networks2000science socialnets Using data from computer databases of scientific papers in physics, biomedical research, and computer science, we have constructed networks of collaboration between scientists in each of these disciplines. In these networks two scientists are considered connected if they have coauthored one or more papers together. We have studied many statistical properties of our networks, including numbers of papers written by authors, numbers of authors per paper, numbers of collaborators that scientists have, typical distance through the network from one scientist to another, and a variety of measures of connectedness within a network, such as closeness and betweenness. We further argue that simple networks such as these cannot capture the variation in the strength of collaborative ties and propose a measure of this strength based on the number of papers coauthored by pairs of scientists, and the number of other scientists with whom they coauthored those papers. Using a selection of our results, we suggest a variety of possible ways to answer the question "Who is the best connected scientist?"Fri Jun 16 10:34:37 CEST 2006MarEmail as Spectroscopy: Automated Discovery of Community Structure within Organizations2003community socialnets findingcommunities We describe a methodology for the automatic identification of communities of practice from email logs within an organization. We use a betweeness centrality algorithm that can rapidly find communities within a graph representing information flows. We apply this algorithm to an email corpus of nearly one million messages collected over a two-month span, and show that the method is effective at identifying true communities, both formal and informal, within these scale-free graphs. This approach also enables the identification of leadership roles within the communities. These studies are complemented by a qualitative evaluation of the results in the field.Fri Jun 16 10:34:37 CEST 2006FebDefining and identifying communities in networks2004socialnets findingcommunities community 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.Fri Jun 16 10:34:37 CEST 2006New York, NY, USAKDD '01: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining57--66Mining the network value of customers2001socialnets socialnetsCommunity for tag(s) socialnets