Engineering Graph Clustering : Models and Experimental Evaluation

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BRANDES, Ulrik, Marco GAERTLER, Dorothea WAGNER, 2007. Engineering Graph Clustering : Models and Experimental Evaluation. In: ACM Journal of Experimental Algorithmics. 12, 1.1. Available under: doi: 10.1145/1227161.1227162

@article{Brandes2007Engin-5928, title={Engineering Graph Clustering : Models and Experimental Evaluation}, year={2007}, doi={10.1145/1227161.1227162}, volume={12}, journal={ACM Journal of Experimental Algorithmics}, author={Brandes, Ulrik and Gaertler, Marco and Wagner, Dorothea}, note={Article Number: 1.1} }

<rdf:RDF xmlns:dcterms="" xmlns:dc="" xmlns:rdf="" xmlns:bibo="" xmlns:dspace="" xmlns:foaf="" xmlns:void="" xmlns:xsd="" > <rdf:Description rdf:about=""> <bibo:uri rdf:resource=""/> <dc:contributor>Wagner, Dorothea</dc:contributor> <dc:creator>Gaertler, Marco</dc:creator> <dc:date rdf:datatype="">2011-03-24T16:01:26Z</dc:date> <dcterms:rights rdf:resource=""/> <dc:language>eng</dc:language> <dcterms:hasPart rdf:resource=""/> <dcterms:bibliographicCitation>First publ. in: ACM Journal of Experimental Algorithmics 12 (2007), Article 1.1</dcterms:bibliographicCitation> <dcterms:available rdf:datatype="">2011-03-24T16:01:26Z</dcterms:available> <dcterms:isPartOf rdf:resource=""/> <dcterms:abstract xml:lang="eng">A promising approach to graph clustering is based on the intuitive notion of intracluster density versus intercluster sparsity. As for the weighted case, clusters should accumulate lots of weight, in contrast to their connection to the remaining graph, which should be light. 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. In order to deepen the understanding of particular concepts, including both quality assessment as well as designing new algorithms, we conducted an experimental evaluation of graph-clustering approaches. By combining proved techniques from graph partitioning and geometric clustering, we also introduce a new approach that compares favorably.</dcterms:abstract> <dspace:isPartOfCollection rdf:resource=""/> <dc:rights>Attribution-NonCommercial-NoDerivs 2.0 Generic</dc:rights> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dc:contributor>Gaertler, Marco</dc:contributor> <dc:format>application/pdf</dc:format> <dc:creator>Brandes, Ulrik</dc:creator> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dspace:hasBitstream rdf:resource=""/> <dc:creator>Wagner, Dorothea</dc:creator> <dc:contributor>Brandes, Ulrik</dc:contributor> <dcterms:title>Engineering Graph Clustering : Models and Experimental Evaluation</dcterms:title> <dcterms:issued>2007</dcterms:issued> </rdf:Description> </rdf:RDF>

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