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Finding Representative Nodes in Probabilistic Graphs

Finding Representative Nodes in Probabilistic Graphs

  • Laura Langohr5 &
  • Hannu Toivonen5 
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  • 6 Citations

Part of the Lecture Notes in Computer Science book series (LNAI,volume 7250)

Abstract

We introduce the problem of identifying representative nodes in probabilistic graphs, motivated by the need to produce different simple views to large BisoNets. We define a probabilistic similarity measure for nodes, and then apply clustering methods to find groups of nodes. Finally, a representative is output from each cluster. We report on experiments with real biomedical data, using both the k-medoids and hierarchical clustering methods in the clustering step. The results suggest that the clustering based approaches are capable of finding a representative set of nodes.

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Authors and Affiliations

  1. Department of Computer Science and Helsinki Institute for Information Technology HIIT, University of Helsinki, Finland

    Laura Langohr & Hannu Toivonen

Authors
  1. Laura Langohr
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  2. Hannu Toivonen
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Editors and Affiliations

  1. Department of Computer and Information Science, University of Konstanz, Konstanz, Germany

    Michael R. Berthold

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Langohr, L., Toivonen, H. (2012). Finding Representative Nodes in Probabilistic Graphs. In: Berthold, M.R. (eds) Bisociative Knowledge Discovery. Lecture Notes in Computer Science(), vol 7250. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31830-6_15

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  • DOI: https://doi.org/10.1007/978-3-642-31830-6_15

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