Scalable online betweenness centrality in evolving graphs

Nicolas Kourtellis, Gianmarco Morales, Francesco Bonchi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

Betweenness centrality measures the importance of an element of a graph, either a vertex or an edge, by the fraction of shortest paths that pass through it [1]. This measure is notoriously expensive to compute, and the best known algorithm, proposed by Brandes [2], runs in O(nm) time. The problems of efficiency and scalability are exacerbated in a dynamic setting, where the input is an evolving graph seen edge by edge, and the goal is to keep the betweenness centrality up to date. In this paper [8] we propose the first truly scalable and practical framework for computing vertex and edge betweenness centrality of large evolving graphs, incrementally and online.

Original languageEnglish
Title of host publication2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1580-1581
Number of pages2
ISBN (Electronic)9781509020195
DOIs
Publication statusPublished - 22 Jun 2016
Externally publishedYes
Event32nd IEEE International Conference on Data Engineering, ICDE 2016 - Helsinki, Finland
Duration: 16 May 201620 May 2016

Other

Other32nd IEEE International Conference on Data Engineering, ICDE 2016
CountryFinland
CityHelsinki
Period16/5/1620/5/16

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Graphics and Computer-Aided Design
  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management

Cite this

Kourtellis, N., Morales, G., & Bonchi, F. (2016). Scalable online betweenness centrality in evolving graphs. In 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016 (pp. 1580-1581). [7498421] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDE.2016.7498421