  Recent decades have witnessed the tremendous development of network science,
which indeed brings a new and insightful language to model real systems of
different domains. Betweenness, a widely employed centrality in network
science, is a decent proxy in investigating network loads and rankings.
However, the extremely high computational cost greatly prevents its applying on
large networks. Though several parallel algorithms have been presented to
reduce its calculation cost on unweighted networks, a fast solution for
weighted networks, which are in fact more ubiquitous than unweighted ones in
reality, is still missing. In this study, we develop an efficient parallel
GPU-based approach to boost the calculation of betweenness centrality on very
large and weighted networks. Comprehensive and systematic evaluations on both
synthetic and real-world networks demonstrate that our solution can arrive the
performance of 30x to 150x speedup over the CPU implementation by integrating
the work-efficient and warp-centric strategies. Our algorithm is completely
open-sourced and free to the community and it is public available through
https://dx.doi.org/10.6084/m9.figshare.4542405. Considering the pervasive
deployment and declining price of GPU on personal computers and servers, our
solution will indeed offer unprecedented opportunities for exploring the
betweenness related problems in network science.
