Publikation:

Eigensolver Methods for Progressive Multidimensional Scaling of Large Data

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Datum

2007

Autor:innen

Pich, Christian

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URI (zitierfähiger Link)
http://nbn-resolving.de/urn:nbn:de:bsz:352-opus-73423
DOI (zitierfähiger Link)
https://doi.org/10.1007/978-3-540-70904-6_6
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Attribution-NonCommercial-NoDerivs 2.0 Generic

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Open Access Green

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Published

Erschienen 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_6

Zusammenfassung

We 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.

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ISO 690BRANDES, 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_6
BibTex
@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}
}
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