Open access
2015 Proceedings of the Seventeenth Workshop on Algorithm Engineering and Experiments (ALENEX)

(Semi-)External Algorithms for Graph Partitioning and Clustering


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.

Formats available

You can view the full content in the following formats:

Information & Authors


Published In

cover image Proceedings
2015 Proceedings of the Seventeenth Workshop on Algorithm Engineering and Experiments (ALENEX)
Pages: 33 - 43
Editors: Ulrik Brandes, University of Konstanz, Germany and David Eppstein, University of California, Irvine, USA
ISBN (Online): 978-1-61197-375-4


Published online: 22 December 2014



Metrics & Citations



If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

Cited By

There are no citations for this item

View Options

View options


View PDF

Get Access







Copy the content Link

Share with email

Email a colleague

Share on social media

The SIAM Publications Library now uses SIAM Single Sign-On for individuals. If you do not have existing SIAM credentials, create your SIAM account