BibSonomy publications for /user/stumme/AnalysisWed Jan 04 11:24:21 CET 2012Working Notes of the LWA 2011 - Learning, Knowledge, AdaptationFace-to-Face Contacts during LWA 2010 - Communities, Roles, and Key Players20112011 LWA analysis conferator contacts itegpub myown Wed Jan 04 11:23:25 CET 2012Proc. Workshop on Mining Ubiquitous and Social Environments (MUSE 2011) at ECML/PKDD 2011Face-to-Face Contacts during a Conference: Communities, Roles, and Key Players20112011 analysis communities community conferator discovery itegpub knowledge myown rfid Wed Dec 15 11:43:00 CET 2010Toronto, CanadaProceedings of the 21st ACM conference on Hypertext and hypermediaVisit me, click me, be my friend: An analysis of evidence networks of user relationships in Bibsonomy20102010 analysis bibsonomy evidence itegpub l3s links myown networks semantic sna web Wed May 19 11:55:51 CEST 2010New York, NY, USAHT '08: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia157--166Logsonomy - Social Information Retrieval with Logdata20082.0 2008 analysis folksonomy information itegpub logsonomy myown network retrieval search social tagorapub web web2.0 web20 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.Fri Nov 27 21:23:21 CET 2009HeidelbergKI 2001: Advances in Artificial Intelligence. KI 2001335-350LNAIIntelligent Structuring and Reducing of Association Rules and with Formal Concept Analysis217420012001 FCA OntologyHandbook analysis association bases closed concept condensed discovery fca formal itemsets kdd knowledge mining myown representations rule rules Fri Nov 13 14:51:05 CET 2009New York, NY, USASSM '08: Proceeding of the 2008 ACM workshop on Search in social media67--74Efficient sampling of information in social networks2008analysis network networks sampling sna social As online social networking emerges, there has been increased interest to utilize the underlying social structure as well as the available social information to improve search. In this paper, we focus on improving the performance of information collection from the neighborhood of a user in a dynamic social network. To this end, we introduce sampling based algorithms to quickly approximate quantities of interest from the vicinity of a user's social graph. We then introduce and analyze variants of this basic scheme exploring correlations across our samples. Models of centralized and distributed social networks are considered. We show that our algorithms can be utilized to rank items in the neighborhood of a user, assuming that information for each user in the network is available. Using real and synthetic data sets, we validate the results of our analysis and demonstrate the efficiency of our algorithms in approximating quantities of interest. The methods we describe are general and can probably be easily adopted in a variety of strategies aiming to efficiently collect information from a social graph.Mon Oct 19 16:52:47 CEST 2009New York, NY, USAWWW '09: Proceedings of the 18th international conference on World wide web731--740Network analysis of collaboration structure in Wikipedia2009analysis collaboration network seminar2009 sna social wikipedia In this paper we give models and algorithms to describe and analyze the collaboration among authors of Wikipedia from a network analytical perspective. The edit network encodes who interacts how with whom when editing an article; it significantly extends previous network models that code author communities in Wikipedia. Several characteristics summarizing some aspects of the organization process and allowing the analyst to identify certain types of authors can be obtained from the edit network. Moreover, we propose several indicators characterizing the global network structure and methods to visualize edit networks. It is shown that the structural network indicators are correlated with quality labels of the associated Wikipedia articles.Fri Oct 16 12:37:56 CEST 2009New York, NY, USAWWW '08: Proceeding of the 17th international conference on World Wide Web915--924Planetary-scale views on a large instant-messaging network2008analysis messenger msn network seminar2009 sna social We present a study of anonymized data capturing a month of high-level communication activities within the whole of the Microsoft Messenger instant-messaging system. We examine characteristics and patterns that emerge from the collective dynamics of large numbers of people, rather than the actions and characteristics of individuals. The dataset contains summary properties of 30 billion conversations among 240 million people. From the data, we construct a communication graph with 180 million nodes and 1.3 billion undirected edges, creating the largest social network constructed and analyzed to date. We report on multiple aspects of the dataset and synthesized graph. We find that the graph is well-connected and robust to node removal. We investigate on a planetary-scale the oft-cited report that people are separated by "six degrees of separation" and find that the average path length among Messenger users is 6.6. We find that people tend to communicate more with each other when they have similar age, language, and location, and that cross-gender conversations are both more frequent and of longer duration than conversations with the same gender.Wed Jul 29 13:46:38 CEST 2009Computational Science – ICCS 20061114-1117Collaborative Tagging as a Tripartite Network2006analysis collaborative folksonomy networks sna social tagging tripartite We describe online collaborative communities by tripartite networks, the nodes being persons, items and tags. We introduce projection methods in order to uncover the structures of the networks, i.e. communities of users, genre families... The structuring of the network is visualised by using a tree representation. The notion of diversity in the system is also discussed.Tue Jul 14 22:34:36 CEST 2009Amsterdam, The Netherlands, The NetherlandsWeb Semant.138--53Discovering shared conceptualizations in folksonomies62008analysis concept fca formal myown ontologyhandbook trias Social bookmarking tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. Unlike ontologies, shared conceptualizations are not formalized, but rather implicit. We present a new data mining task, the mining of all frequent tri-concepts, together with an efficient algorithm, for discovering these implicit shared conceptualizations. Our approach extends the data mining task of discovering all closed itemsets to three-dimensional data structures to allow for mining folksonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution. Finally, we show the applicability of our approach on three large real-world examples.Fri Jul 03 00:26:53 CEST 2009HeidelbergConceptual Structures: Logical, Linguistic, and Computational Issues. Proc. ICCS '00421-437LNAIConceptual Knowledge Discovery and Data Analysis186720002000 FCA OntologyHandbook analysis ckdd concept conceptual discovery fca formal kdd knowledge myown Fri Jul 03 00:23:48 CEST 2009Heidelberg-BerlinPrinciples of Data Mining and Knowledge Discovery. Proc. PKDD 2000367-374LNAI{CEM} -- A Program for Visualization and Discovery in Email191020002000 analysis cem concept email fca formal mail management manager myown nepomuk Fri Jul 03 00:19:24 CEST 2009Universität Dortmund 763Proc. GI-Fachgruppentreffen Maschinelles Lernen (FGML'01)OctoberConceptual Clustering with Iceberg Concept Lattices20012001 analysis closed clustering concept conceptual discovery fca formal iceberg itemsets kdd knowledge lattices myown Fri Jul 03 00:14:04 CEST 2009HeidelbergFormal Concept Analysis: Foundations and Applications180-195LNAIEfficient Mining of Association Rules Based on Formal Concept Analysis362620052005 analysis association book closed concept condensed data discovery fca formal itegpub itemsets kdd knowledge l3s mining myown representations rules Association rules are a popular knowledge discovery technique for warehouse basket analysis. They indicate which items of the warehouse are frequently bought together. The problem of association rule mining has first been stated in 1993. Five years later, several research groups discovered that this problem has a strong connection to Formal Concept Analysis (FCA). In this survey, we will first introduce some basic ideas of this connection along a specific algorithm, \titanic, and show how FCA helps in reducing the number of resulting rules without loss of information, before giving a general overview over the history and state of the art of applying FCA for association rule mining.Thu Jun 18 12:01:46 CEST 2009Social network analysis2005Netzwerkanalyse Soziologie analysis network sna social Literaturverz. S. [193] - 204Thu Jun 18 11:23:58 CEST 2009Structural analysis in the social sciencesModels and methods in social network analysis2007Mathematisches Modell Netzwerkanalyse Soziologie analysis methods models network sna social LiteraturangabenTue May 26 16:18:59 CEST 2009Encyclopedia of Complexity and System ScienceSocial Network Analysis, Large-scale2009analysis batagelj network sna social Mon Mar 02 21:40:27 CET 2009Hong KongProceedings of the 6th IEEE International Conference on Data Mining (ICDM 06)December907-911TRIAS - An Algorithm for Mining Iceberg Tri-Lattices20062006 FCA OntologyHandbook algorithm analysis concept fca folksonomies folksonomy formal iceberg itegpub lattices myown nepomuk tagging tri triadic trias Mon May 26 11:57:05 CEST 2008Machine Learning#aug#295--122A lattice conceptual clustering system and its application to browsing retrieval241996analysis carpineto clustering concept fca formal information ir retrieval The theory of concept (or Galois) lattices provides a simple and formal approach to conceptual clustering. In this paper we present GALOIS, a system that automates and applies this theory. The algorithm utilized by GALOIS to build a concept lattice is incremental and efficient, each update being done in time at most quadratic in the number of objects in the lattice. Also, the algorithm may incorporate background information into the lattice, and through clustering, extend the scope of the theory. The application we present is concerned with information retrieval via browsing, for which we argue that concept lattices may represent major support structures. We describe a prototype user interface for browsing through the concept lattice of a document-term relation, possibly enriched with a thesaurus of terms. An experimental evaluation of the system performed on a medium-sized bibliographic database shows good retrieval performance and a significant improvement after the introduction of background knowledge. ER -Fri Mar 28 17:54:33 CET 2008Journal of Web Semantics138-53Discovering Shared Conceptualizations in Folksonomies620082008 FCA OntologyHandbook analysis bibsonomy concept discovering fca folksonomies formal itegpub l3s myown shared triadic