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Synergies of Soft Computing and Statistics for Intelligent Data Analysis pp 563–571Cite as

Clustering on Dynamic Social Network Data

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 190))

Abstract

This paper presents a reference data set along with a labeling for graph clustering algorithms, especially for those handling dynamic graph data. We implemented a modification of Iterative Conductance Cutting and a spectral clustering. As base data set we used a filtered part of the Enron corpus. Different cluster measurements, as intra-cluster density, inter-cluster sparseness, and Q-Modularity were calculated on the results of the clustering to be able to compare results from other algorithms.

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Author information

Authors and Affiliations

  1. Faculty of Computer Science, Otto-von-Guericke University of Magdeburg, Magdeburg, Germany

    Pascal Held & Kai Dannies

Authors
  1. Pascal Held
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  2. Kai Dannies
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Corresponding author

Correspondence to Pascal Held .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science, Otto-von-Guericke University of Magdebur, Geb. 29, Raum 008, Universitätsplatz 2, Magdeburg, 39106, Germany

    Rudolf Kruse

  2. , FB Informatik & Informationswissenschaft, University of Konstanz, Konstanz, 78457, Germany

    Michael R. Berthold

  3. of Magdeburg, Faculty of Computer Science, Otto-von-Guericke University, Geb. 29, Universitätsplatz 2 008, Magdeburg, 39106, Germany

    Christian Moewes

  4. , Department of Statistics and OR, University of Oviedo, C/ Calvo Sotelo, s/n, Oviedo, 33007, Spain

    María Ángeles Gil

  5. Systems Research Institute, Polish Academy of Sciences, Newelska 6, Warsaw, 01-447, Poland

    Przemysław Grzegorzewski

  6. Systems Research Institute, Polish Academy of Sciences, Newelska 6, Warsaw, 01-447, Poland

    Olgierd Hryniewicz

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Held, P., Dannies, K. (2013). Clustering on Dynamic Social Network Data. In: Kruse, R., Berthold, M., Moewes, C., Gil, M., Grzegorzewski, P., Hryniewicz, O. (eds) Synergies of Soft Computing and Statistics for Intelligent Data Analysis. Advances in Intelligent Systems and Computing, vol 190. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33042-1_60

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  • DOI: https://doi.org/10.1007/978-3-642-33042-1_60

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33041-4

  • Online ISBN: 978-3-642-33042-1

  • eBook Packages: EngineeringEngineering (R0)

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