HPC Lab

BMI - OSU





HPC Lab

BADIOS: Framework for Fast Betweenness Centrality

General information

Who is more important in a network? Who controls the flow between the nodes or whose contribution is significant for connections? Centrality metrics play an important role while answering these questions. The betweenness metric has always been intriguing and used in many analysis. Yet, it is one of the most computationally expensive kernels in graph mining. For that reason, making betweenness centrality computations faster is an important and well-studied problem. In this work, we propose the framework, BADIOS, which compresses a network and shatters it into pieces so that the centrality computation can be handled independently for each piece. Although BADIOS is designed and tuned for betweenness centrality, it can easily be adapted for other centrality metrics. Experimental results show that the proposed techniques can be a great arsenal to reduce the centrality computation time for various types and sizes of networks. In particular, it reduces the computation time of a graph with 4.6 million edges from more than 5 days to less than 16 hours. If you use BADIOS, please cite