Comparison of algorithms in graph partitioning

Alain Guénoche

RAIRO - Operations Research - Recherche Opérationnelle (2008)

  • Volume: 42, Issue: 4, page 469-484
  • ISSN: 0399-0559

Abstract

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We first describe four recent methods to cluster vertices of an undirected non weighted connected graph. They are all based on very different principles. The fifth is a combination of classical ideas in optimization applied to graph partitioning. We compare these methods according to their ability to recover classes initially introduced in random graphs with more edges within the classes than between them.

How to cite

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Guénoche, Alain. "Comparison of algorithms in graph partitioning." RAIRO - Operations Research - Recherche Opérationnelle 42.4 (2008): 469-484. <http://eudml.org/doc/245844>.

@article{Guénoche2008,
abstract = {We first describe four recent methods to cluster vertices of an undirected non weighted connected graph. They are all based on very different principles. The fifth is a combination of classical ideas in optimization applied to graph partitioning. We compare these methods according to their ability to recover classes initially introduced in random graphs with more edges within the classes than between them.},
author = {Guénoche, Alain},
journal = {RAIRO - Operations Research - Recherche Opérationnelle},
keywords = {graph partitioning; partition comparison; simulation},
language = {eng},
number = {4},
pages = {469-484},
publisher = {EDP-Sciences},
title = {Comparison of algorithms in graph partitioning},
url = {http://eudml.org/doc/245844},
volume = {42},
year = {2008},
}

TY - JOUR
AU - Guénoche, Alain
TI - Comparison of algorithms in graph partitioning
JO - RAIRO - Operations Research - Recherche Opérationnelle
PY - 2008
PB - EDP-Sciences
VL - 42
IS - 4
SP - 469
EP - 484
AB - We first describe four recent methods to cluster vertices of an undirected non weighted connected graph. They are all based on very different principles. The fifth is a combination of classical ideas in optimization applied to graph partitioning. We compare these methods according to their ability to recover classes initially introduced in random graphs with more edges within the classes than between them.
LA - eng
KW - graph partitioning; partition comparison; simulation
UR - http://eudml.org/doc/245844
ER -

References

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