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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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
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