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

A GPU-Based Solution to Fast Calculation of Betweenness Centrality on Large Weighted Networks