BeBeCA contains exact betweenness centrality scores for large datasets to be used as ground truth in evaluating approximate betweenness centrality algorithms. These scores were computed using a parallel implementation of Brandes algorithm executed on a supercomputer. Computing these results on a high end commodity machine would take many years of continuous computation!
For betweenness centrality users, BeBeCA offers guidance on which approximation algorithm suites what applications.
For betweenness centrality researchers, BeBeCA offers exact betweenness centrality scores for large datasets. If you are researching new ways of approximating betweenness centrality, consider using BeBeCA to evaluate your methods.
BeBeCA contains scripts for computing a set of metrics that charactarize the quality of an approximation algorithm.
Ziyad AlGhamdi, Fuad Jamour, Spiros Skiadopoulos, and Panos Kalnis. A Benchmark for Betweenness Centrality Approximation Algorithms on Large Graphs. In 29th SSDBM International Conference on Scientific and Statistical Database Management. 2017.