Hierarchical parallel algorithm for modularity-based community detection using GPUs

29Citations
Citations of this article
19Readers
Mendeley users who have this article in their library.

This article is free to access.

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. © 2013 Springer-Verlag.

Cite

CITATION STYLE

APA

Cheong, C. Y., Huynh, H. P., Lo, D., & Goh, R. S. M. (2013). Hierarchical parallel algorithm for modularity-based community detection using GPUs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8097 LNCS, pp. 775–787). https://doi.org/10.1007/978-3-642-40047-6_77

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free