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Parallel Community Detection for Massive Graphs
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BibTeX
@MISC{Riedy_parallelcommunity,
author = {E. Jason Riedy and Henning Meyerhenke and David Ediger and Davida. Bader},
title = {Parallel Community Detection for Massive Graphs},
year = {}
}
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Abstract
Abstract. Tackling the current volume of graph-structured data requires parallel tools. We extend our work on analyzing such massive graph data with the first massively parallel algorithm for community detection that scales to current data sizes, scaling to graphs of over 122 million vertices and nearly 2 billion edges in under 7300 seconds on a massively multithreaded Cray XMT. Our algorithm achieves moderate parallel scalability without sacrificing sequential operational complexity. Community detection partitions a graph into subgraphs more densely connected within the subgraph than to the rest of the graph. We take an agglomerative approach similar to Clauset, Newman, and Moore’s sequential algorithm, merging pairs of connected intermediate subgraphs to optimize different graph properties. Working in parallel opens new approaches to high performance. On smaller data sets, we find the output’s modularity compares well with the standard sequential algorithms.
Keyphrases
parallel community detection massive graph current data size community detection sequential algorithm parallel scalability parallel open new approach graph-structured data algorithm achieves sequential operational complexity standard sequential algorithm high performance cray xmt output modularity connected intermediate subgraphs different graph property massive graph data community detection partition parallel tool current volume parallel algorithm data set agglomerative approach
