The documents distributed by this server have been provided by the contributing authors as a means to ensure timely dissemination of scholarly and technical work on a noncommercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

E. Solomonik, M. Besta, F. Vella, T. Hoefler:
	Scaling Betweenness Centrality using Communication-Efficient Sparse Matrix Multiplication (Nov. 2017, Accepted at The International Conference for High Performance Computing, Networking, Storage and Analysis (SC'17) ) Abstract Betweenness centrality (BC) is a crucial graph problem that measures the significance of a vertex by the number of shortest paths leading through it. We propose Maximal Frontier Betweenness Centrality (MFBC): a succinct BC algorithm based on novel sparse matrix multiplication routines that performs a factor of p 1/3 less communication on p processors than the best known alternatives, for graphs with n vertices and average degree k = n/p 2/3 . We formulate, implement, and prove the correctness of MFBC for weighted graphs by leveraging monoids instead of semirings, which enables a surprisingly succinct formulation. MFBC scales well for both extremely sparse and relatively dense graphs. It automatically searches a space of distributed data decompositions and sparse matrix multiplication algorithms for the most advantageous configuration. The MFBC im- plementation outperforms the well-known CombBLAS library by up to 8x and shows more robust performance. Our design methodology is readily extensible to other graph problems. Documents download article:
	BibTeX
@inproceedings{,   author={E. Solomonik and M. Besta and F. Vella and T. Hoefler},   title={{Scaling Betweenness Centrality using Communication-Efficient Sparse Matrix Multiplication}},   year={2017},   month={Nov.},   note={Accepted at The International Conference for High Performance Computing, Networking, Storage and Analysis (SC'17)},   source={http://www.unixer.de/~htor/publications/}, }