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Communication Dans Un Congrès Année : 2016

KADABRA is an ADaptive Algorithm for Betweenness via Random Approximation

Résumé

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 Theta(|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, as well.

Dates et versions

hal-02002600 , version 1 (14-02-2019)

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Michele Borassi, Emanuele Natale. KADABRA is an ADaptive Algorithm for Betweenness via Random Approximation. 24th Annual European Symposium on Algorithms (ESA 2016), Aug 2016, Aarhus, Denmark. ⟨10.4230/LIPIcs.ESA.2016.20⟩. ⟨hal-02002600⟩
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