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Journal Articles ACM Journal of Experimental Algorithmics Year : 2019

KADABRA is an ADaptive Algorithm for Betweenness via Random Approximation


We present KADABRA, a new algorithm to approximate betweenness centrality in directed and undirected graphs, which significantly outperforms all previous approaches on real-world complex networks. The efficiency of the new algorithm relies on two new theoretical contributions, of independent interest. The first contribution focuses on sampling shortest paths, a subroutine used by most algorithms that approximate betweenness centrality. We show that, on realistic random graph models, we can perform this task in time |E| 1 2 +o (1) with high probability, obtaining a significant speedup with respect to the Θ(|E|) worst-case performance. We experimentally show that this new technique achieves similar speedups on real-world complex networks, as well. The second contribution is a new rigorous application of the adaptive sampling technique. This approach decreases the total number of shortest paths that need to be sampled to compute all betweenness centralities with a given absolute error, and it also handles more general problems, such as computing the k most central nodes. Furthermore, our analysis is general, and it might be extended to other settings.
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Dates and versions

hal-02043367 , version 1 (11-07-2019)



Michele Borassi, Emanuele Natale. KADABRA is an ADaptive Algorithm for Betweenness via Random Approximation. ACM Journal of Experimental Algorithmics, 2019, 24 (1), ⟨10.1145/3284359⟩. ⟨hal-02043367⟩
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