Betweenness Centrality (BC) is a widely used metric of the relevance of a node in a network. The fastest-known algorithm for the evaluation of BC on unweighted graphs builds a tree representing information about the shortest paths for each vertex to calculate its contribution to the BC score. Actually, for specific vertices, the shortest-path trees of neighboring nodes could be leveraged to reduce the computational burden, but existing BC algorithms do not exploit that information and carry out redundant computations. We propose a new algorithm, called dynamic merging of frontiers, which makes use of such information to derive the BC score of degree-2 vertices by re-using the results of the sub-trees of the neighbors. We implemented our idea in parallel fashion exploiting Graphics Processing Units. Compared to state-of-the-art implementations, our approach achieves a linear improvement in the number of degree-2 vertices and an average improvement of × over a variety of real-world graphs.

Supplemental movie and image files for, Dynamic Merging of Frontiers for Accelerating the Evaluation of Betweenness Centrality