License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.ESA.2016.20
URN: urn:nbn:de:0030-drops-63712
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2016/6371/
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Borassi, Michele ; Natale, Emanuele

KADABRA is an ADaptive Algorithm for Betweenness via Random Approximation

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LIPIcs-ESA-2016-20.pdf (0.7 MB)

Abstract

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.

BibTeX - Entry

@InProceedings{borassi_et_al:LIPIcs:2016:6371,
  author =	{Michele Borassi and Emanuele Natale},
  title =	{{KADABRA is an ADaptive Algorithm for Betweenness via Random Approximation}},
  booktitle =	{24th Annual European Symposium on Algorithms (ESA 2016)},
  pages =	{20:1--20:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-015-6},
  ISSN =	{1868-8969},
  year =	{2016},
  volume =	{57},
  editor =	{Piotr Sankowski and Christos Zaroliagis},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2016/6371},
  URN =		{urn:nbn:de:0030-drops-63712},
  doi =		{10.4230/LIPIcs.ESA.2016.20},
  annote =	{Keywords: Betweenness centrality, shortest path algorithm, graph mining, sampling, network analysis}
}

Keywords: Betweenness centrality, shortest path algorithm, graph mining, sampling, network analysis
Collection: 24th Annual European Symposium on Algorithms (ESA 2016)
Issue Date: 2016
Date of publication: 18.08.2016

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