RT Journal Article
T1 A Spark parallel betweenness centrality computation and its application to community detection problems
A1 Gómez González, Daniel
A1 Llana Díaz, Luis Fernando
A1 Pareja Flores, Cristóbal
AB 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.
PB Graz University of Technology
SN 0948-695X
YR 2022
FD 2022-02
LK https://hdl.handle.net/20.500.14352/112337
UL https://hdl.handle.net/20.500.14352/112337
LA eng
NO 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
NO Agencia estatal de investigación
NO Comunidad de Madrid
NO Unión Europea
DS Docta Complutense
RD 10 abr 2025