(Semi-)External Algorithms for Graph Partitioning and Clustering

Abstract

Previous chapter Next chapter Full AccessProceedings 2015 Proceedings of the Meeting on Algorithm Engineering and Experiments (ALENEX)(Semi-)External Algorithms for Graph Partitioning and ClusteringYaroslav Akhremtsev, Peter Sanders, and Christian SchulzYaroslav Akhremtsev, Peter Sanders, and Christian Schulzpp.33 - 43Chapter DOI:https://doi.org/10.1137/1.9781611973754.4PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract In this paper, we develop semi-external and external memory algorithms for graph partitioning and clustering problems. Graph partitioning and clustering are key tools for processing and analyzing large complex networks. We address both problems in the (semi-)external model by adapting the size-constrained label propagation technique. Our (semi-)external size-constrained label propagation algorithm can be used to compute graph clusterings and is a prerequisite for the (semi-)external graph partitioning algorithm. The algorithm is then used for both the coarsening and the refinement phase of a multilevel algorithm to compute graph partitions. Our algorithm is able to partition and cluster huge complex networks with billions of edges on cheap commodity machines. Experiments demonstrate that the semi-external graph partitioning algorithm is scalable and can compute high quality partitions in time that is comparable to the running time of an efficient internal memory implementation. A parallelization of the algorithm in the semi-external model further reduces running time. Previous chapter Next chapter RelatedDetails Published:2015eISBN:978-1-61197-375-4 https://doi.org/10.1137/1.9781611973754Book Series Name:ProceedingsBook Code:PRAL15Book Pages:1-187