Paper Info
Title
Scalable Community Detection with the Louvain Algorithm
Abstract
In this paper we present and evaluate a parallel community detection algorithm derived from the state-of-the-art Louvain modularity maximization method. Our algorithm adopts a novel graph mapping and data representation, and relies on can efficient communication runtime, specifically designed for fine-grained applications executed on large-scale supercomputers. We have been able to parallelize graphs with up to 138 billion edges on 8, 192 Blue Gene/Q nodes and 1, 024 P7-IH nodes. Leveraging the convergence properties of our algorithm and the efficient implementation, we can analyze communities of large scale graphs in just a few seconds. To the best of our knowledge, this is the first parallel implementation of the Louvain algorithm that scales to these large data and processor configurations.
Year
DOI
Venue
2015
10.1109/IPDPS.2015.59
2015 IEEE International Parallel and Distributed Processing Symposium
Keywords
Field
DocType
scalable community detection,Louvain algorithm,parallel community detection algorithm,Louvain modularity maximization method,graph mapping,data representation,fine-grained applications,large-scale supercomputers,parallelize graphs
Convergence (routing),Graph,External Data Representation,Computer science,Blue gene,Parallel computing,Algorithm,Theoretical computer science,Maximization,Modularity,Scalability,Distributed computing
Conference
ISSN
Citations 
PageRank 
1530-2075
15
0.78
References 
Authors
29
4
Name
Order
Citations
PageRank
Xinyu Que112411.81
Fabio Checconi219714.03
Fabrizio Petrini32050165.82
John A. Gunnels471783.20