文章基本信息

标题：A Spark Parallel Betweenness Centrality Computation and its Application to Community Detection Problems
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作者：Daniel Gomez González ; Luis Llana Díaz ; Cristóbal Pareja 等
期刊名称：Journal of Universal Computer Science
印刷版ISSN：0948-6968
出版年度：2022
卷号：28
期号：2
页码：160-180
DOI：10.3897/jucs.80688
语种：English
出版社：Graz University of Technology and Know-Center
摘要：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.