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. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Lang, K. J., Mahoney, M. W., & Orecchia, L. (2009). Empirical evaluation of graph partitioning using spectral embeddings and flow. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5526 LNCS, pp. 197–208). https://doi.org/10.1007/978-3-642-02011-7_19
Mendeley helps you to discover research relevant for your work.