Gómez González, DanielLlana Díaz, Luis FernandoPareja Flores, Cristóbal2024-12-102024-12-102022-02Gomez González, Daniel, et al. “A Spark Parallel Betweenness Centrality Computation and its Application to Community Detection Problems”. JUCS - Journal of Universal Computer Science, vol. 28, núm. 2, febrero de 2022, pp. 160–80. DOI.org (Crossref), https://doi.org/10.3897/jucs.806880948-695X10.3897/jucs.80688https://hdl.handle.net/20.500.14352/112337The Brandes algorithm has the lowest computational complexity for computing the betweenness centrality measures of all nodes or edges in a given graph. Its numerous applications make it one of the most used algorithms in social network analysis. In this work, we provide a parallel version of the algorithm implemented in Spark. The experimental results show that the parallel algorithm scales as the number of cores increases. Finally, we provide a version of the well-known community detection Girvan-Newman algorithm, based on the Spark version of Brandes algorithm.engAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/journal article0948-6968https://doi.org/10.3897/jucs.80688https://lib.jucs.org/article/80688/open access004SparkMapReduceSocial Network AnalysisCentrality measureBrandes AlgorithmDistributed programmingInformática (Informática)1203.17 Informática