Open Access
Lecture Notes in Computer Science, pages 197-208
Empirical Evaluation of Graph Partitioning Using Spectral Embeddings and Flow
1
Yahoo! Research, Santa Clara, USA
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Publication type: Book Chapter
Publication date: 2009-06-02
Journal:
Lecture Notes in Computer Science
Q2
SJR: 0.606
CiteScore: 2.6
Impact factor: —
ISSN: 03029743, 16113349, 18612075, 18612083
Abstract
We present initial results from the first empirical evaluation of a graph partitioning algorithm inspired by the Arora-Rao-Vazirani algorithm of [5], which combines spectral and flow methods in a novel way. We have studied the parameter space of this new algorithm, e.g., examining the extent to which different parameter settings interpolate between a more spectral and a more flow-based approach, and we have compared results of this algorithm to results from previously known and optimized algorithms such as Metis.
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Lang K. J., Mahoney M. W., Orecchia L. Empirical Evaluation of Graph Partitioning Using Spectral Embeddings and Flow // Lecture Notes in Computer Science. 2009. pp. 197-208.
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Lang K. J., Mahoney M. W., Orecchia L. Empirical Evaluation of Graph Partitioning Using Spectral Embeddings and Flow // Lecture Notes in Computer Science. 2009. pp. 197-208.
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TY - GENERIC
DO - 10.1007/978-3-642-02011-7_19
UR - https://doi.org/10.1007/978-3-642-02011-7_19
TI - Empirical Evaluation of Graph Partitioning Using Spectral Embeddings and Flow
T2 - Lecture Notes in Computer Science
AU - Lang, Kevin J.
AU - Mahoney, Michael W.
AU - Orecchia, Lorenzo
PY - 2009
DA - 2009/06/02
PB - Springer Nature
SP - 197-208
SN - 0302-9743
SN - 1611-3349
SN - 1861-2075
SN - 1861-2083
ER -
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@incollection{2009_Lang,
author = {Kevin J. Lang and Michael W. Mahoney and Lorenzo Orecchia},
title = {Empirical Evaluation of Graph Partitioning Using Spectral Embeddings and Flow},
publisher = {Springer Nature},
year = {2009},
pages = {197--208},
month = {jun}
}