Empirical Evaluation of Graph Partitioning Using Spectral Embeddings and Flow

Authors:
Kevin J. Lang;Michael W. Mahoney;Lorenzo Orecchia
Affiliations:
Yahoo! Research, Santa Clara, USA;Stanford University, Stanford, USA;University of California, Berkeley, USA
Venue:
SEA '09 Proceedings of the 8th International Symposium on Experimental Algorithms
Year:
2009

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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 .