Miscellaneous
Density-based Temporal Graph Clustering for Subgroup Detection in Social Networks.
2007. Presented at The 4th conference on Applications of Social Network Analysis (ASNA).
Tanja Falkowski and Anja Barth.
[BibTeX]
Density-based Temporal Graph Clustering for Subgroup Detection in Social Networks.
2007. Presented at The 4th conference on Applications of Social Network Analysis (ASNA).
Tanja Falkowski and Anja Barth.
[BibTeX]
Conference articles
Conceptual Clustering of Social Bookmark Sites.
In: A. Hinneburg, editor,
Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007), pages 50-54.
Martin-Luther-Universität Halle-Wittenberg, 2007.
Miranda Grahl, Andreas Hotho and Gerd Stumme.
[doi]
[BibTeX]
Conceptual Clustering of Social Bookmark Sites.
In: A. Hinneburg, editor,
Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007), pages 50-54.
Martin-Luther-Universität Halle-Wittenberg, 2007.
Miranda Grahl, Andreas Hotho and Gerd Stumme.
[doi]
[BibTeX]
Conceptual Clustering of Social Bookmark Sites.
In: A. Hinneburg, editor,
Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007), pages 50-54.
Martin-Luther-Universität Halle-Wittenberg, 2007.
Miranda Grahl, Andreas Hotho and Gerd Stumme.
[doi]
[BibTeX]
Conceptual Clustering of Social Bookmarking Sites.
In:
7th International Conference on Knowledge Management (I-KNOW '07), pages 356-364.
Know-Center, Graz, Austria, 2007.
Miranda Grahl, Andreas Hotho and Gerd Stumme.
[doi]
[abstract]
[BibTeX]
Currently, social bookmarking systems provide intuitive support for browsing locally their content. A global view is usually presented by the tag cloud of the system, but it does not allow a conceptual drill-down, e. g., along a conceptual hierarchy. In this paper, we present a clustering approach for computing such a conceptual hierarchy for a given folksonomy. The hierarchy is complemented with ranked lists of users and resources most related to each cluster. The rankings are computed using our FolkRank algorithm. We have evaluated our approach on large scale data from the del.icio.us bookmarking system.
Conceptual Clustering of Social Bookmarking Sites.
In:
7th International Conference on Knowledge Management (I-KNOW '07), pages 356-364.
Know-Center, Graz, Austria, 2007.
Miranda Grahl, Andreas Hotho and Gerd Stumme.
[abstract]
[BibTeX]
Currently, social bookmarking systems provide intuitive support for browsing locally their content. A global view is usually presented by the tag cloud of the system, but it does not allow a conceptual drill-down, e. g., along a conceptual hierarchy. In this paper, we present a clustering approach for computing such a conceptual hierarchy for a given folksonomy. The hierarchy is complemented with ranked lists of users and resources most related to each cluster. The rankings are computed using our FolkRank algorithm. We have evaluated our approach on large scale data from the del.icio.us bookmarking system.
Technical reports
Collaborative Creation of Communal Hierarchical Taxonomies in Social Tagging Systems.
Computer Science Department, 2006. Number 2006-10.
Paul Heymann and Hector Garcia-Molina.
[doi]
[abstract]
[BibTeX]
Collaborative tagging systems---systems where many casual users annotate objects with free-form strings (tags) of their choosing---have recently emerged as a powerful way to label and organize large collections of data. During our recent investigation into these types of systems, we discovered a simple but remarkably effective algorithm for converting a large corpus of tags annotating objects in a tagging system into a navigable hierarchical taxonomy of tags. We first discuss the algorithm and then present a preliminary model to explain why it is so effective in these types of systems.
Journal articles
Fast algorithm for detecting community structure in networks.
Physical Review E, 69, 2003.
M.E.J. Newman.
[doi]
[BibTeX]