Abstract
Today, online social networks have millions of users, and continue growing up. For that reason, the graphs generated from these networks usually do not fit into a single machine’s memory and the time required for its processing is very large. In particular, to compute a centrality measure like betweenness could be expensive on those graphs. To address this challenge, in this paper we present a parallel and distributed algorithm to compute betweenness. Also, we develop a heuristic to reduce the overall time, which accomplish a speedup over 80x in the best of cases.
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CITATION STYLE
Campuzano-Alvarez, M., & Fonseca-Bruzón, A. (2016). Distributed and parallel algorithm for computing betweenness centrality. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10022 LNAI, pp. 285–296). Springer Verlag. https://doi.org/10.1007/978-3-319-47955-2_24
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