Clustering on Dynamic Social Network Data

  • Pascal Held
  • Kai Dannies
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 190)


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.


Clustering cluster measurements Enron data set graph clustering stream data 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Faculty of Computer ScienceOtto-von-Guericke University of MagdeburgMagdeburgGermany

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