2026-08-22T19:39:27Zhttps://docta.ucm.es/rest/oai/request

oai:docta.ucm.es:20.500.14352/1123372025-03-18T12:50:14Zcom_20.500.14352_14col_20.500.14352_15

A Spark parallel betweenness centrality computation and its application to community detection problems Gómez González, Daniel Llana Díaz, Luis Fernando Pareja Flores, Cristóbal 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. 2024-12-10T13:38:19Z 2024-12-10T13:38:19Z 2022-02 journal article Gomez 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.80688 0948-695X 10.3897/jucs.80688 https://hdl.handle.net/20.500.14352/112337 0948-6968 https://doi.org/10.3897/jucs.80688 https://lib.jucs.org/article/80688/ eng RTI2018-093608-B-C3 S2018/TCS-4314 S2018/TCS-4314 http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 International Graz University of Technology