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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Campuzano-Alvarez, M., Fonseca-Bruzón, A. (2016). Distributed and Parallel Algorithm for Computing Betweenness Centrality. In: Montes y Gómez, M., Escalante, H., Segura, A., Murillo, J. (eds) Advances in Artificial Intelligence - IBERAMIA 2016. IBERAMIA 2016. Lecture Notes in Computer Science(), vol 10022. Springer, Cham. https://doi.org/10.1007/978-3-319-47955-2_24
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DOI: https://doi.org/10.1007/978-3-319-47955-2_24
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