Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
User interactions in social networks and their implications
Proceedings of the 4th ACM European conference on Computer systems
A measurement-driven analysis of information propagation in the flickr social network
Proceedings of the 18th international conference on World wide web
Parallel community detection on large networks with propinquity dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Real World Routing Using Virtual World Information
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Mapping search relevance to social networks
Proceedings of the 3rd Workshop on Social Network Mining and Analysis
Fast Community Detection Algorithm with GPUs and Multicore Architectures
IPDPS '11 Proceedings of the 2011 IEEE International Parallel & Distributed Processing Symposium
An efficient algorithm for community mining with overlap in social networks
Expert Systems with Applications: An International Journal
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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.