Daniel Gomez González‡, Luis Liana Díaz‡, Cristóbal Pareja‡Corresponding author: Daniel Gomez González ( dagomez@estad.ucm.es ) Corresponding author: Luis Liana Díaz ( llana@ucm.es ) Corresponding author: Cristóbal Pareja ( cpareja@ucm.es ) © Daniel Gomez González, Luis Liana Díaz, Cristóbal Pareja. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY-ND 4.0). This license allows reusers to copy and distribute the material in any medium or format in unadapted form only, and only so long as attribution is given to the creator. The license allows for commercial use. Citation:
Gomez González D, Liana Díaz L, Pareja C (2022) A Spark Parallel Betweenness Centrality Computation and its Application to Community Detection Problems. JUCS - Journal of Universal Computer Science 28(2): 160-180. https://doi.org/10.3897/jucs.80688 |  |
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.