@incollection{yu2000social, abstract = {Trust is important wherever agents must interact. We consider the important case of interactions in electronic communities, where the agents assist and represent principal entities, such as people and businesses. We propose a social mechanism of reputation management, which aims at avoiding interaction with undesirable participants. Social mechanisms complement hard security techniques (such as passwords and digital certificates), which only guarantee that a party is authenticated and authorized, but do not ensure that it exercises its authorization in a way that is desirable to others. Social mechanisms are even more important when trusted third parties are not available. Our specific approach to reputation management leads to a decentralized society in which agents help each other weed out undesirable players.}, address = {Berlin/Heidelberg}, affiliation = {Department of Computer Science, North Carolina State University, Raleigh, NC 27695-7534, USA}, author = {Yu, Bin and Singh, Munindar}, booktitle = {Cooperative Information Agents IV - The Future of Information Agents in Cyberspace}, doi = {10.1007/978-3-540-45012-2_15}, editor = {Klusch, Matthias and Kerschberg, Larry}, interhash = {1065a4963600ef4f9b4c034d3bbd9a50}, intrahash = {337afcb67138b927b27a9687199e8568}, isbn = {978-3-540-67703-1}, keyword = {Computer Science}, pages = {355--393}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {A Social Mechanism of Reputation Management in Electronic Communities}, url = {http://dx.doi.org/10.1007/978-3-540-45012-2_15}, volume = 1860, year = 2000 } @inproceedings{hristova2012mapping, abstract = {Communities of people are better mappers if they are spatially clustered, as revealed in an interesting new paper by Hristova, Mashhadi, Quattrone and Capra from UCL. "This preliminary analysis inspires further inquiry because it shows a clear correlation between spatial affiliation, the internal community structure and the community’s engagement in terms of coverage", according to the authors. They have studied the similarity patterns among eight hundred contributors to OpenStreetMap, the well-known crowdmapping project and detected the hidden community structure. It is a very promising field of research, coupling a social network analysis of crowdsourced data. Participants to such projects are rarely independent individuals: in most cases, they involve communities more than single participants and it would be crucial to uncover how the underlying social structure reflects on the quantity and the quality of the collected data. It has the greatest relevance for citizen science projects, as data quality is often the key issue determining the success or the failure of the collective effort. }, author = {Hristova, Desislava and Mashhadi, Afra and Quattrone, Giovanni and Capra, Licia}, booktitle = {Proc. When the City Meets the Citizen Workshop (WCMCW)}, interhash = {373e02fe56d30b26261a33135e0b7a45}, intrahash = {f0a69ac56b94a471b470ebd56545fafd}, month = jun, title = {Mapping Community Engagement with Urban Crowd-Sourcing}, url = {http://www.cs.ucl.ac.uk/staff/l.capra/publications/wcmcw12.pdf}, year = 2012 } @article{zhang2011emergence, abstract = {Social and community intelligence research aims to reveal individual and group behaviors, social interactions, and community dynamics by mining the digital traces that people leave while interacting with Web applications, static infrastructure, and mobile and wearable devices.}, author = {Zhang, Daqing and Guo, Bin and Yu, Zhiwen}, doi = {10.1109/MC.2011.65}, interhash = {6f31ae148e44ea9b78624a1822a36c44}, intrahash = {83e180e95544a7b35d0914de61e98621}, issn = {0018-9162}, journal = {Computer}, month = jul, number = 7, pages = {21--28}, publisher = {IEEE}, title = {The Emergence of Social and Community Intelligence}, volume = 44, year = 2011 } @inproceedings{doerfel2012publication, abstract = {We present an analysis of the publication and citation networks of all previous editions of the three conferences most relevant to the FCA community: ICFCA, ICCS and CLA. Using data mining methods from FCA and graph analysis, we investigate patterns and communities among authors, we identify and visualize influential publications and authors, and we give a statistical summary of the conferences’ history. }, address = {Berlin/Heidelberg}, author = {Doerfel, Stephan and Jäschke, Robert and Stumme, Gerd}, booktitle = {Formal Concept Analysis}, doi = {10.1007/978-3-642-29892-9_12}, editor = {Domenach, F. and Ignatov, D.I. and Poelmans, J.}, interhash = {f34f31e8dd1e07b1b0a5ab688f10084a}, intrahash = {9207cd4b1cf7d87c9ae959ac780e152c}, isbn = {978-3-642-29891-2}, month = may, pages = {77--95}, publisher = {Springer}, series = {Lecture Notes in Artificial Intelligence}, title = {Publication Analysis of the Formal Concept Analysis Community}, url = {http://link.springer.com/chapter/10.1007/978-3-642-29892-9_12}, volume = 7278, year = 2012 } @article{newman2006modularity, abstract = {Many networks of interest in the sciences, including social networks, computer networks, and metabolic and regulatory networks, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community structure is one of the outstanding issues in the study of networked systems. One highly effective approach is the optimization of the quality function known as “modularity” over the possible divisions of a network. Here I show that the modularity can be expressed in terms of the eigenvectors of a characteristic matrix for the network, which I call the modularity matrix, and that this expression leads to a spectral algorithm for community detection that returns results of demonstrably higher quality than competing methods in shorter running times. I illustrate the method with applications to several published network data sets.}, author = {Newman, M. E. J.}, doi = {10.1073/pnas.0601602103}, interhash = {e664336d414a1e21d89f30cc56f5e739}, intrahash = {5dd9d0c2155f242393e63547d8a2347f}, journal = {Proceedings of the National Academy of Sciences}, number = 23, pages = {8577--8582}, title = {Modularity and community structure in networks}, volume = 103, year = 2006 } @inproceedings{freyne2007collecting, abstract = {The goal of this paper is to detail the integration of two "social Web" technologies - social search and social navigation - and to highlight the benefits of such integration on two levels. Firstly, both technologies harvest and harness "community wisdom" and in an integrated system each of the search and navigation components can benefit from the additional community wisdom gathered by the other when assisting users to locate relevant information. Secondly, by integrating search and browsing we facilitate the development of a unique interface that effectively blends search and browsing functionality as part of a seamless social information access service. This service allows users to effectively combine their search and browsing behaviors. In this paper we will argue that this integration provides significantly more than the simple sum of the parts.}, acmid = {1216312}, address = {New York, NY, USA}, author = {Freyne, Jill and Farzan, Rosta and Brusilovsky, Peter and Smyth, Barry and Coyle, Maurice}, booktitle = {Proceedings of the 12th international conference on Intelligent user interfaces}, doi = {10.1145/1216295.1216312}, interhash = {871e012dc7b1c131d32480f1e3a655e7}, intrahash = {93fecd064cd42e0ea5f9dc06a9458d3c}, isbn = {1-59593-481-2}, location = {Honolulu, Hawaii, USA}, numpages = {10}, pages = {52--61}, publisher = {ACM}, title = {Collecting community wisdom: integrating social search \& social navigation}, url = {http://doi.acm.org/10.1145/1216295.1216312}, year = 2007 } @inproceedings{mitzlaff2010visit, abstract = {The ongoing spread of online social networking and sharing sites has reshaped the way how people interact with each other. Analyzing the relatedness of different users within the resulting large populations of these systems plays an important role for tasks like user recommendation or community detection. Algorithms in these fields typically face the problem that explicit user relationships (like friend lists) are often very sparse. Surprisingly, implicit evidences (like click logs) of user relations have hardly been considered to this end. Based on our long-time experience with running BibSonomy [4], we identify in this paper different evidence networks of user relationships in our system. We broadly classify each network based on whether the links are explicitly established by the users (e.g., friendship or group membership) or accrue implicitly in the running system (e.g., when user u copies an entry of user v). We systematically analyze structural properties of these networks and whether topological closeness (in terms of the length of shortest paths) coincides with semantic similarity between users.}, address = {New York, NY, USA}, author = {Mitzlaff, Folke and Benz, Dominik and Stumme, Gerd and Hotho, Andreas}, booktitle = {HT '10: Proceedings of the 21st ACM Conference on Hypertext and Hypermedia}, doi = {10.1145/1810617.1810664}, interhash = {5584c4c57fcd8eb4663df8b114bcf09c}, intrahash = {6628bf43e3834ba147a22992f2f534e9}, isbn = {978-1-4503-0041-4}, location = {Toronto, Ontario, Canada}, pages = {265--270}, publisher = {ACM}, title = {Visit me, click me, be my friend: an analysis of evidence networks of user relationships in BibSonomy}, url = {http://portal.acm.org/citation.cfm?id=1810617.1810664}, year = 2010 } @article{barber2007mac, abstract = {The modularity of a network quantifies the extent, relative to a null model network, to which vertices cluster into community groups. We define a null model appropriate for bipartite networks, and use it to define a bipartite modularity. The bipartite modularity is presented in terms of a modularity matrix B; some key properties of the eigenspectrum of B are identified and used to describe an algorithm for identifying modules in bipartite networks. The algorithm is based on the idea that the modules in the two parts of the network are dependent, with each part mutually being used to induce the vertices for the other part into the modules. We apply the algorithm to real-world network data, showing that the algorithm successfully identifies the modular structure of bipartite networks.}, author = {Barber, M. J.}, doi = {10.1103/PhysRevE.76.066102}, interhash = {e1d9f528c49b34ff4a05b2b0060bd653}, intrahash = {61f9d5839845d5d8fa1883a46a2f7744}, journal = {Physical Review E}, number = 6, title = {Modularity and community detection in bipartite networks}, url = {http://arxiv.org/abs/arXiv:0707.1616}, volume = 76, year = 2007 } @article{guimera2007mib, abstract = {Modularity is one of the most prominent properties of real-world complex networks. Here, we address the issue of module identification in two important classes of networks: bipartite networks and directed unipartite networks. Nodes in bipartite networks are divided into two non-overlapping sets, and the links must have one end node from each set. Directed unipartite networks only have one type of nodes, but links have an origin and an end. We show that directed unipartite networks can be conviniently represented as bipartite networks for module identification purposes. We report a novel approach especially suited for module detection in bipartite networks, and define a set of random networks that enable us to validate the new approach.}, author = {Guimer{\`a}, R. and Sales-Pardo, M. and Amaral, L.A.N.}, doi = {10.1103/PhysRevE.76.036102}, interhash = {a87821c7c8e7d5ca89cb369e6215a0f3}, intrahash = {6145a42fe04aee556fa7a68c7cea7db3}, journal = {Physical review. E, Statistical, nonlinear, and soft matter physics}, number = {3 Pt 2}, pages = 036102, publisher = {NIH Public Access}, title = {Module identification in bipartite and directed networks}, url = {http://arxiv.org/abs/physics/0701151}, volume = 76, year = 2007 } @inproceedings{Detecting_Commmunities_via_Simultaneous_Clustering_of_Graphs_and_Folksonomies, author = {Java, Akshay and Joshi, Anupam and Finin, Tim}, booktitle = {WebKDD 2008 Workshop on Web Mining and Web Usage Analysis}, interhash = {acfec953843b168e61e2e167e29b4c3d}, intrahash = {645abd6b3191a2a6e844d7542651ed1c}, month = {August}, note = {To Appear}, title = {Detecting Commmunities via Simultaneous Clustering of Graphs and Folksonomies}, year = 2008 } @article{duch-2005-72, abstract = {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.}, author = {Duch, J. and Arenas, A.}, interhash = {2e37e9b6a0f76e94125990a47cd287f3}, intrahash = {36d905c5223e5516db9d08eb3e0bc9fc}, journal = {Physical Review E}, pages = 027104, title = {Community detection in complex networks using Extremal Optimization}, url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cond-mat/0501368}, volume = 72, year = 2005 } @book{koch2003unterstuetzung, abstract = {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.}, address = {Frankfurt am Main}, author = {Koch, Jürgen Hartmut}, interhash = {7159d1b552a883a54a7ca0e8ab299d33}, intrahash = {683e13d82b21ff7ebb4afcc20958f762}, isbn = {978-3-631-50288-4}, note = {PhD Thesis (2002)}, number = 39, publisher = {Peter Lang Publishing Group}, school = {Technische Universität München}, series = {Europäische Hochschulschriften}, title = {Unterstützung der Formierung und Analyse von virtuellen Communities}, volume = 41, year = 2003 } @techreport{carotenuto1999communityspace, abstract = {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.}, author = {Carotenuto, Linda and Etienne, William and Fontaine, Michael and Friedman, Jessica and Muller, Michael and Newberg, Helene and Simpson, Matthew and Slusher, Jason and Stevenson, Kenneth}, institution = {IBM Watson Research Center}, interhash = {0b2fee4b78c51114478e698f0dc40b13}, intrahash = {a0abdf3ed0cac548f4dd5f8e073b6314}, month = {April}, number = {99-04}, title = {Community Space: Toward Flexible Support for Voluntary Knowledge Communities}, year = 1999 } @inproceedings{fischer2001communities, address = {Ulvik, Hardanger Fjord, Norway}, author = {Fischer, Gerhard}, booktitle = {24th annual Information Systems Research Seminar in Scandinavia}, interhash = {790e778dcd713ffb84756a6734d1097a}, intrahash = {2b58a1bff72a7440c43786fc4c1493b0}, month = {August}, pages = 2001, title = {Communities of Interest: Learning through the Interaction of Multiple Knowledge Systems}, year = 2001 } @techreport{ieKey, author = {Groh, Georg}, institution = {TU München}, interhash = {7507ea3706a7cc5aaae769370f0671b1}, intrahash = {87835f6fce05f443a4956673662734d2}, month = {March}, title = {Ortsbezug in kontext-sensitiven Diensten für mobile Communities}, type = {8. Münchner Fortbildungsseminar Geoinformationssysteme}, year = 2003 } @inproceedings{jaeschke06wege, abstract = {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.}, address = {Halle-Wittenberg}, author = {Jäschke, Robert and Hotho, Andreas and Schmitz, Christoph and Stumme, Gerd}, booktitle = {Proc. 18. Workshop Grundlagen von Datenbanken}, editor = {Braß, Stefan and Hinneburg, Alexander}, interhash = {59224b5889a24108434a9b5ecc6b0887}, intrahash = {2b6be3bd5daee7119973fcf69909956f}, month = {June}, pages = {80-84}, publisher = {Martin-Luther-Universität }, title = {Wege zur Entdeckung von Communities in Folksonomies}, url = {http://www.kde.cs.uni-kassel.de/jaeschke/pub/jaeschke2006wege_gvd.pdf}, year = 2006 } @phdthesis{trier05visualization, author = {Trier, Matthias}, interhash = {f36769dd1fffe61d9239e4b4b7dc40e9}, intrahash = {66eb70a04e6946077182446170dd6dcf}, 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}, url = {http://nbn-resolving.de/urn/resolver.pl?urn=urn:nbn:de:kobv:83-opus-10720}, year = 2005 } @inproceedings{citeulike:391307, address = {Arlington, VA, USA}, author = {Rosen-Zvi, Michal and Griffiths, Thomas and Steyvers, Mark and Smyth, Padhraic}, booktitle = {Proceedings of the 20th conference on Uncertainty in artificial intelligence}, citeulike-article-id = {391307}, interhash = {79b4ff1335f13cdbe18a38086e9fab3b}, intrahash = {a4dd688efe5778fb99ff94de104211aa}, isbn = {0974903906}, pages = {487--494}, priority = {0}, publisher = {AUAI Press}, title = {The author-topic model for authors and documents}, url = {http://portal.acm.org/citation.cfm?id=1036843.1036902}, year = 2004 } @inproceedings{conf/sdm/AggarwalY05, author = {Aggarwal, Charu C. and Yu, Philip S.}, booktitle = {SDM}, interhash = {e1487d660a1614b50bd756f7383b98ea}, intrahash = {bb72c8baa786e98565c4a7448ecae59a}, title = {Online Analysis of Community Evolution in Data Streams.}, url = {http://web.mit.edu/charu/www/aggar142.pdf }, year = 2005 } @misc{almeida03design, author = {Almeida, R.B. and Almeida, V.A.F.}, booktitle = {Proceedings of the 4th International Conference on Internet Computing}, interhash = {c882373d278260ba31ae4142e4f6e664}, intrahash = {41d2e7ad7417153fa5cb257486468919}, pages = {17--23}, title = {Design and evaluation of a user-based community discovery technique}, url = {citeseer.ist.psu.edu/almeida03design.html}, year = 2003 }