Title

Hierarchical Parallel Algorithm for Modularity-Based Community Detection Using GPUs

Publication Type

Conference Proceeding Article

Publication Date

8-2013

Abstract

This paper describes the design of a hierarchical parallel algorithm for accelerating community detection which involves partitioning a network into communities of densely connected nodes. The algorithm is based on the Louvain method developed at the Université Catholique de Louvain, which uses modularity to measure community quality and has been successfully applied on many different types of networks. The proposed hierarchical parallel algorithm targets three levels of parallelism in the Louvain method and it has been implemented on single-GPU and multi-GPU architectures. Benchmarking results on several large web-based networks and popular social networks show that on top of offering speedups of up to 5x, the single-GPU version is able to find better quality communities. On average, the multi-GPU version provides an additional 2x speedup over the single-GPU version but with a 3% degradation in community quality.

Keywords

Community detection, parallel algorithm, GPU, social networks

Discipline

Software Engineering

Research Areas

Software Systems

Publication

Euro-Par 2013 Parallel Processing: 19th International Conference, Aachen, Germany, August 26-30, 2013. Proceedings

Volume

8097

First Page

775

Last Page

787

ISBN

9783642400476

Identifier

10.1007/978-3-642-40047-6_77

Publisher

Springer Verlag

City or Country

Aachen, Germany

Additional URL

http://dx.doi.org/10.1007/978-3-642-40047-6_77

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