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.
This work was partially supported by the DFG under grant BR 2158/1-1 and WA 654/13-1 and EU under grant IST-2001-33555 COSIN.