Empirical Evaluation of Graph Partitioning Using Spectral Embeddings and Flow

被引：0
|
作者：
Lang, Kevin J. [1 ]
Mahoney, Michael W. [2 ]
Orecchia, Lorenzo [3 ]
机构：
[1] Yahoo Res, Santa Clara, CA USA
[2] Stanford Univ, Stanford, CA 94305 USA
[3] Univ Calif Berkeley, Berkeley, CA 94720 USA
来源：
EXPERIMENTAL ALGORITHMS, PROCEEDINGS | 2009年 / 5526卷
基金：
美国国家科学基金会;
关键词：
ALGORITHMS; QUALITY;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
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.
引用
收藏
页码：197 / +
页数：2
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