2026-07-25T00:41:23Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:6371 2024-03-06T10:38:05Z ddc:004 open_access

KADABRA is an ADaptive Algorithm for Betweenness via Random Approximation Borassi, Michele Natale, Emanuele Betweenness centrality shortest path algorithm graph mining sampling network analysis 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Michele Borassi and Emanuele Natale 2016 Is Part Of LIPIcs, Volume 57, 24th Annual European Symposium on Algorithms (ESA 2016) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.ESA.2016.20 urn:nbn:de:0030-drops-63712 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2016.20 eng https://creativecommons.org/licenses/by/3.0/legalcode