Scalable online betweenness centrality in evolving graphs

Betweenness centrality measures the importance of an element of a graph, either a vertex or an edge, by the fraction of shortest paths that pass through it [1]. This measure is notoriously expensive to compute, and the best known algorithm, proposed by Brandes [2], runs in O(nm) time. The problems of efficiency and scalability are exacerbated in a dynamic setting, where the input is an evolving graph seen edge by edge, and the goal is to keep the betweenness centrality up to date. In this paper [8] we propose the first truly scalable and practical framework for computing vertex and edge betweenness centrality of large evolving graphs, incrementally and online.