Parallel Graph Partitioning for Complex Networks

Processing large complex networks like social networks or web graphs has attracted considerable interest. To do this in parallel, we need to partition them into pieces of roughly equal size. Unfortunately, previous parallel graph partitioners originally developed for more regular mesh-like networks...

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Veröffentlicht in:IEEE transactions on parallel and distributed systems Jg. 28; H. 9; S. 2625 - 2638
Hauptverfasser: Meyerhenke, Henning, Sanders, Peter, Schulz, Christian
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.09.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
algorithm design and analysis
Clustering
Clustering algorithms
Clusters
Coarsening
combinatorial algorithms
Complex networks
Evolutionary algorithms
Evolutionary computation
graph algorithms
Graph theory
Load balancing and task assignment
Multilevel
Parallel processing
Partitioning
Partitioning algorithms
Partitions
Propagation
Social network services
Social networks
Sociology
Statistics
Structural hierarchy
ISSN:1045-9219, 1558-2183
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Zusammenfassung:Processing large complex networks like social networks or web graphs has attracted considerable interest. To do this in parallel, we need to partition them into pieces of roughly equal size. Unfortunately, previous parallel graph partitioners originally developed for more regular mesh-like networks do not work well for complex networks. Here we address this problem by parallelizing and adapting the label propagation technique originally developed for graph clustering. By introducing size constraints, label propagation becomes applicable for both the coarsening and the refinement phase of multilevel graph partitioning. This way we exploit the hierarchical cluster structure present in many complex networks. We obtain very high quality by applying a highly parallel evolutionary algorithm to the coarsest graph. The resulting system is both more scalable and achieves higher quality than state-of-theart systems like ParMetis or PT-Scotch. For large complex networks the performance differences are very big. As an example, our algorithm partitions a web graph with 3.3 G edges in 16 seconds using 512 cores of a high-performance cluster while producing a high quality partition-none of the competing systems can handle this graph on our system.
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ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2017.2671868