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• W2488391916 abstract "Community detection is an important data clustering technique for studying graph structures. Many serial algorithms have been developed and well studied in the literature. As the problem size grows, the research attention has recently been turning to parallelizing the technique. However, the conventional parallelization strategies that divide the problem domain into non-overlapping subdomains do not scale with problem size and the number of processes. The main obstacle lies in the fact that the graph algorithms often exhibit a high degree of data dependency, which makes developing scalable parallel algorithms a great challenge." @default.
• W2488391916 created "2016-08-23" @default.
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• W2488391916 date "2016-01-01" @default.
• W2488391916 modified "2023-09-29" @default.
• W2488391916 title "Parallel Community Detection Algorithm Using a Data Partitioning Strategy with Pairwise Subdomain Duplication" @default.
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• W2488391916 doi "https://doi.org/10.1007/978-3-319-41321-1_6" @default.
• W2488391916 hasPublicationYear "2016" @default.
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