Falkowski, T. & Barth, A.: Density-based Temporal Graph Clustering for Subgroup Detection in Social Networks. , 2007

Falkowski, T. & Barth, A.: Density-based Temporal Graph Clustering for Subgroup Detection in Social Networks. , 2007

Grahl, M.; Hotho, A. & Stumme, G.: Conceptual Clustering of Social Bookmark Sites. In: Hinneburg, A. (Hrsg.): Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007). Martin-Luther-Universität Halle-Wittenberg, 2007, S. 50-54
[Volltext]

Grahl, M.; Hotho, A. & Stumme, G.: Conceptual Clustering of Social Bookmark Sites. In: Hinneburg, A. (Hrsg.): Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007). Martin-Luther-Universität Halle-Wittenberg, 2007, S. 50-54
[Volltext]

Grahl, M.; Hotho, A. & Stumme, G.: Conceptual Clustering of Social Bookmark Sites. In: Hinneburg, A. (Hrsg.): Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007). Martin-Luther-Universität Halle-Wittenberg, 2007, S. 50-54
[Volltext]

Grahl, M.; Hotho, A. & Stumme, G.: Conceptual Clustering of Social Bookmarking Sites. 7th International Conference on Knowledge Management (I-KNOW '07). Graz, Austria: Know-Center, 2007, S. 356-364
[Volltext]
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.

Grahl, M.; Hotho, A. & Stumme, G.: Conceptual Clustering of Social Bookmarking Sites. 7th International Conference on Knowledge Management (I-KNOW '07). Graz, Austria: Know-Center, 2007, S. 356-364

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.

Heymann, P. & Garcia-Molina, H.: Collaborative Creation of Communal Hierarchical Taxonomies in Social Tagging Systems. , 2006
[Volltext]
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.

Newman, M.: Fast algorithm for detecting community structure in networks. In: Physical Review E 69 (2003),
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