Parallel Graph Partitioning for Complex Networks
This enables efficient parallel processing of large complex networks like social or web graphs, addressing a bottleneck in high-performance computing.
The paper tackles the problem of partitioning large complex networks for parallel processing by parallelizing and adapting label propagation with size constraints, achieving high scalability and quality. For example, it partitions a web graph with 3.3 billion edges in under 16 seconds on 512 cores, outperforming state-of-the-art systems like ParMetis and PT-Scotch.
Processing large complex networks like social networks or web graphs has recently attracted considerable interest. In order to do this in parallel, we need to partition them into pieces of about equal size. Unfortunately, previous parallel graph partitioners originally developed for more regular mesh-like networks do not work well for these networks. This paper addresses 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. We obtain very high quality by applying a highly parallel evolutionary algorithm to the coarsened graph. The resulting system is both more scalable and achieves higher quality than state-of-the-art systems like ParMetis or PT-Scotch. For large complex networks the performance differences are very big. For example, our algorithm can partition a web graph with 3.3 billion edges in less than sixteen 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.