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

页码：197 / +

页数：2

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