One way to overcome this problem are social bookmark tools, which are rapidly emerging on the web. In such systems, users are setting up lightweight conceptual structures called folksonomies, and overcome thus the knowledge acquisition bottleneck. As more and more people participate in the effort, the use of a common vocabulary becomes more and more stable. We present an approach for discovering topic-specific trends within folksonomies. It is based on a differential adaptation of the PageRank algorithm to the triadic hypergraph structure of a folksonomy. The approach allows for any kind of data, as it does not rely on the internal structure of the documents. In particular, this allows to consider different data types in the same analysis step. We run experiments on a large-scale real-world snapshot of a social bookmarking system. ER - TY - CONF AU - Jäschke, Robert AU - Hotho, Andreas AU - Schmitz, Christoph AU - Stumme, Gerd A2 - Braß, Stefan A2 - Hinneburg, Alexander T1 - Wege zur Entdeckung von Communities in Folksonomies T2 - Proc. 18. Workshop Grundlagen von Datenbanken PB - Martin-Luther-Universität CY - Halle-Wittenberg PY - 2006/06 M2 - VL - IS - SP - 80 EP - 84 UR - http://www.kde.cs.uni-kassel.de/jaeschke/pub/jaeschke2006wege_gvd.pdf M3 - KW - 2006 KW - community KW - detection KW - folksonomy KW - iccs_example KW - l3s KW - myown KW - trias_example L1 - SN - N1 - N1 - AB - 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. ER - TY - CONF AU - Aggarwal, Charu C. AU - Yu, Philip S. A2 - T1 - Online Analysis of Community Evolution in Data Streams. T2 - SDM PB - CY - PY - 2005/ M2 - VL - IS - SP - EP - UR - http://web.mit.edu/charu/www/aggar142.pdf M3 - KW - data KW - detection KW - stream KW - analysis KW - community L1 - SN - N1 - N1 - AB - ER - TY - JOUR AU - Duch, J. AU - Arenas, A. T1 - Community detection in complex networks using Extremal Optimization JO - Physical Review E PY - 2005/ VL - 72 IS - SP - EP - UR - http://www.citebase.org/abstract?id=oai:arXiv.org:cond-mat/0501368 M3 - KW - community KW - complex KW - detection KW - network L1 - SN - N1 - Citebase - Community detection in complex networks using Extremal Optimization N1 - AB - 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. ER - TY - THES AU - Trier, Matthias T1 - IT-supported Visualization and Evaluation of Virtual Knowledge Communities. Applying Social Network Intelligence Software in Knowledge Management to enable knowledge oriented People Network Management PY - 2005/ PB - SP - EP - UR - http://nbn-resolving.de/urn/resolver.pl?urn=urn:nbn:de:kobv:83-opus-10720 M3 - KW - social KW - detection KW - knowledge KW - management KW - community KW - network L1 - N1 - N1 - AB - ER - TY - CONF AU - Almeida, Rodrigo B. AU - Almeida, Virgilio A. F. A2 - T1 - A community-aware search engine T2 - Proceedings of the 13th international conference on World Wide Web PB - ACM Press CY - New York, NY, USA PY - 2004/ M2 - VL - IS - SP - 413 EP - 421 UR - http://doi.acm.org/10.1145/988672.988728 M3 - KW - search KW - engine KW - detection KW - hits KW - community KW - network L1 - SN - 1-58113-844-X N1 - N1 - AB -
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. ER - TY - GEN AU - Radicchi, Filippo AU - Castellano, Claudio AU - Cecconi, Federico AU - Loreto, Vittorio AU - Parisi, Domenico A2 - T1 - Defining and identifying communities in networks JO - PB - AD - PY - 2004/02 VL - IS - SP - EP - UR - http://arxiv.org/abs/cond-mat/0309488 M3 - KW - graph KW - gn KW - detection KW - network KW - community L1 - N1 - N1 - AB - 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. ER - TY - GEN AU - Almeida, R.B. AU - Almeida, V.A.F. A2 - T1 - Design and evaluation of a user-based community discovery technique JO - PB - AD - PY - 2003/ VL - IS - SP - 17 EP - 23 UR - citeseer.ist.psu.edu/almeida03design.html M3 - KW - detection KW - hits KW - community KW - network L1 - N1 - N1 - AB - ER - TY - CONF AU - Kubica, Jeremy AU - Moore, Andrew AU - Schneider, Jeff A2 - Wu, Xindong A2 - Tuzhilin, Alex A2 - Shavlik, Jude T1 - Tractable Group Detection on Large Link Data Sets T2 - The Third IEEE International Conference on Data Mining PB - IEEE Computer Society CY - PY - 2003/november M2 - VL - IS - SP - 573 EP - 576 UR - M3 - KW - large KW - detection KW - community KW - network KW - gda L1 - SN - N1 - N1 - AB - ER - TY - RPRT AU - Kubica, Jeremy Martin AU - Moore, Andrew AU - Schneider, Jeff A2 - T1 - K-groups: Tractable Group Detection on Large Link Data Sets PB - Robotics Institute, Carnegie Mellon University AD - Pittsburgh, PA PY - 2003/10 VL - IS - CMU-RI-TR-03-32 SP - EP - UR - http://www.ri.cmu.edu/pubs/pub_4489.html M3 - KW - large KW - detection KW - gda KW - network KW - community L1 - N1 - N1 - N1 - AB - 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. ER - TY - CONF AU - Kubica, Jeremy AU - Moore, Andrew AU - Schneider, Jeff AU - Yang, Yiming A2 - T1 - Stochastic Link and Group Detection T2 - Proceedings of the Eighteenth National Conference on Artificial Intelligence PB - AAAI Press/MIT Press CY - PY - 2002/07 M2 - VL - IS - SP - 798 EP - 804 UR - M3 - KW - detection KW - community KW - network KW - gda L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Borodin, Allan AU - Roberts, Gareth O. AU - Rosenthal, Jeffrey S. AU - Tsaparas, Panayiotis A2 - T1 - Finding authorities and hubs from link structures on the World Wide Web T2 - Proceedings of the 10th international conference on World Wide Web PB - ACM Press CY - New York, NY, USA PY - 2001/ M2 - VL - IS - SP - 415 EP - 429 UR - http://doi.acm.org/10.1145/371920.372096 M3 - KW - detection KW - hits KW - community KW - network L1 - SN - N1 - N1 - AB - ER - TY - JOUR AU - Tejada, Sheila AU - Knoblock, Craig A AU - Minton, Steven T1 - Learning object identification rules for information integration JO - Information Systems PY - 2001/12 VL - 26 IS - 8 SP - 607 EP - 633 UR - http://www.sciencedirect.com/science/article/pii/S0306437901000424 M3 - 10.1016/S0306-4379(01)00042-4 KW - detection KW - duplicate KW - entity KW - extraction KW - identification KW - information KW - integration L1 - SN - N1 - N1 - AB - When integrating information from multiple websites, the same data objects can exist in inconsistent text formats across sites, making it difficult to identify matching objects using exact text match. We have developed an object identification system called Active Atlas, which compares the objects’ shared attributes in order to identify matching objects. Certain attributes are more important for deciding if a mapping should exist between two objects. Previous methods of object identification have required manual construction of object identification rules or mapping rules for determining the mappings between objects. This manual process is time consuming and error-prone. In our approach. Active Atlas learns to tailor mapping rules, through limited user input, to a specific application domain. The experimental results demonstrate that we achieve higher accuracy and require less user involvement than previous methods across various application domains. ER -