A promising approach to graph clustering is based on the intuitive notion of intra-cluster density vs. inter-cluster sparsity. While both formalizations and algorithms focusing on particular aspects of this rather vague concept have been proposed no conclusive argument on their appropriateness has been given. As a first step towards understanding the consequences of particular conceptions, we conducted an experimental evaluation of graph clustering approaches. By combining proven techniques from graph partitioning and geometric clustering, we also introduce a new approach that compares favorably. © Springer-Verlag 2003.
CITATION STYLE
Brandes, U., Gaertler, M., & Wagner, D. (2003). Experiments on graph clustering algorithms. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2832, 568–579. https://doi.org/10.1007/978-3-540-39658-1_52
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