Fast Parallel Algorithms for the Modular Decomposition
Fast Parallel Algorithms for the Modular Decomposition
IEEE Transactions on Knowledge and Data Engineering
Parallel community detection on large networks with propinquity dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-level Algorithms for Modularity Clustering
SEA '09 Proceedings of the 8th International Symposium on Experimental Algorithms
Linear time 1/2 -approximation algorithm for maximum weighted matching in general graphs
STACS'99 Proceedings of the 16th annual conference on Theoretical aspects of computer science
A parallel approximation algorithm for the weighted maximum matching problem
PPAM'07 Proceedings of the 7th international conference on Parallel processing and applied mathematics
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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.