Identifying high betweenness centrality nodes in large social networks

This paper proposes an alternative way to identify nodes with high betweenness centrality. It introduces a new metric, k-path centrality, and a randomized algorithm for estimating it, and shows empirically that nodes with high k-path centrality have high node betweenness centrality. The randomized algorithm runs in time O(kappa3n2-2αlog n) and outputs, for each vertex v, an estimate of its k-path centrality up to additive error of ± n1/2+ α with probability 1-1/n2. Experimental evaluations on real and synthetic social networks show improved accuracy in detecting high betweenness centrality nodes and significantly reduced execution time when compared with existing randomized algorithms.