ABPA Ξ AΣ (ABRAXAS): Gnostic word of mystic meaning.

We present ABRA, a suite of algorithms to compute and maintain probabilistically guaranteed high-quality approximations of the betweenness centrality of all nodes (or edges) on both static and fully dynamic graphs. Our algorithms use progressive random sampling and their analysis rely on Rademacher averages and pseudodimension, fundamental concepts from statistical learning theory. To our knowledge, ABRA is the first application of these concepts to the field of graph analysis. Our experimental results show that ABRA is much faster than exact methods, and vastly outperforms, in both runtime number of samples, and accuracy, state-of-the-art algorithms with the same quality guarantees.