%0 Journal Article
%A Gómez González, Daniel
%A Llana Díaz, Luis Fernando
%A Pareja Flores, Cristóbal
%T A Spark parallel betweenness centrality computation and its application to community detection problems
%D 2022
%@ 0948-695X
%U https://hdl.handle.net/20.500.14352/112337
%X The 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.
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