Abstract
Modularity is a popular measure for the quality of a cluster partition. Primarily, its popularity originates from its suitability for community identification through maximization. A lot of algorithms to maximize modularity have been proposed in recent years. Especially agglomerative hierarchical algorithms showed to be fast and find clusterings with high modularity. In this paper we present several of these heuristics, discuss their problems and point out why some algorithms perform better than others. In particular, we analyze the influence of search heuristics on the balancedness of the merge process and show why the uneven contraction of a graph due to an unbalanced merge process leads to clusterings with comparable low modularity.
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Acknowledgements
The research leading to these results has received funding from the European Community’s Seventh Framework Programme FP7/2007-2013 under grant agreement n ∘ 215453 - WeKnowIt.
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Ovelgönne, M., Geyer-Schulz, A. (2012). A Comparison of Agglomerative Hierarchical Algorithms for Modularity Clustering. In: Gaul, W., Geyer-Schulz, A., Schmidt-Thieme, L., Kunze, J. (eds) Challenges at the Interface of Data Analysis, Computer Science, and Optimization. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24466-7_23
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DOI: https://doi.org/10.1007/978-3-642-24466-7_23
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