Eigensolver Methods for Progressive Multidimensional Scaling of Large Data

dc.contributor.authorBrandes, Ulrik
dc.contributor.authorPich, Christiandeu
dc.date.accessioned2011-03-24T15:59:43Zdeu
dc.date.available2011-03-24T15:59:43Zdeu
dc.date.issued2007
dc.description.abstractWe present a novel sampling-based approximation technique for classical multidimensional scaling that yields an extremely fast layout algorithm suitable even for very large graphs. It produces layouts that compare favorably with other methods for drawing large graphs, and it is among the fastest methods available. In addition, our approach allows for progressive computation, i.e. a rough approximation of the layout can be produced even faster, and then be refined until satisfaction.eng
dc.description.versionpublished
dc.format.mimetypeapplication/pdfdeu
dc.identifier.citationFirst publ. in: Proceedings of the 14th International Symposium Graph Drawing (GD ´06) (LNCS 4372), 2007, pp. 42-53deu
dc.identifier.doi10.1007/978-3-540-70904-6_6
dc.identifier.ppn302324976deu
dc.identifier.urihttp://kops.uni-konstanz.de/handle/123456789/5741
dc.language.isoengdeu
dc.legacy.dateIssued2009deu
dc.rightsAttribution-NonCommercial-NoDerivs 2.0 Generic
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.0/
dc.subject.ddc004deu
dc.titleEigensolver Methods for Progressive Multidimensional Scaling of Large Dataeng
dc.typeINPROCEEDINGSdeu
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@inproceedings{Brandes2007Eigen-5741,
  year={2007},
  doi={10.1007/978-3-540-70904-6_6},
  title={Eigensolver Methods for Progressive Multidimensional Scaling of Large Data},
  isbn={978-3-540-70903-9},
  publisher={Springer Berlin Heidelberg},
  address={Berlin, Heidelberg},
  series={Lecture Notes in Computer Science},
  booktitle={Graph Drawing},
  pages={42--53},
  editor={Kaufmann, Michael and Wagner, Dorothea},
  author={Brandes, Ulrik and Pich, Christian}
}
kops.citation.iso690BRANDES, Ulrik, Christian PICH, 2007. Eigensolver Methods for Progressive Multidimensional Scaling of Large Data. In: KAUFMANN, Michael, ed., Dorothea WAGNER, ed.. Graph Drawing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007, pp. 42-53. Lecture Notes in Computer Science. ISBN 978-3-540-70903-9. Available under: doi: 10.1007/978-3-540-70904-6_6deu
kops.citation.iso690BRANDES, Ulrik, Christian PICH, 2007. Eigensolver Methods for Progressive Multidimensional Scaling of Large Data. In: KAUFMANN, Michael, ed., Dorothea WAGNER, ed.. Graph Drawing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007, pp. 42-53. Lecture Notes in Computer Science. ISBN 978-3-540-70903-9. Available under: doi: 10.1007/978-3-540-70904-6_6eng
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kops.sourcefieldKAUFMANN, Michael, ed., Dorothea WAGNER, ed.. <i>Graph Drawing</i>. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007, pp. 42-53. Lecture Notes in Computer Science. ISBN 978-3-540-70903-9. Available under: doi: 10.1007/978-3-540-70904-6_6deu
kops.sourcefield.plainKAUFMANN, Michael, ed., Dorothea WAGNER, ed.. Graph Drawing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007, pp. 42-53. Lecture Notes in Computer Science. ISBN 978-3-540-70903-9. Available under: doi: 10.1007/978-3-540-70904-6_6deu
kops.sourcefield.plainKAUFMANN, Michael, ed., Dorothea WAGNER, ed.. Graph Drawing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007, pp. 42-53. Lecture Notes in Computer Science. ISBN 978-3-540-70903-9. Available under: doi: 10.1007/978-3-540-70904-6_6eng
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source.contributor.editorKaufmann, Michael
source.contributor.editorWagner, Dorothea
source.identifier.isbn978-3-540-70903-9
source.publisherSpringer Berlin Heidelberg
source.publisher.locationBerlin, Heidelberg
source.relation.ispartofseriesLecture Notes in Computer Science
source.titleGraph Drawing

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