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2016 IEEE 32nd International Conference on Data Engineering (ICDE) (2016)
Helsinki, Finland
May 16, 2016 to May 20, 2016
ISBN: 978-1-5090-2020-1
pp: 1580-1581
Nicolas Kourtellis , Telefonica I+D, Barcelona, Spain
Gianmarco De Francisci Morales , Aalto University, Helsinki, Finland
Francesco Bonchi , The ISI Foundation, Torino, Italy
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.
Data structures, Scalability, Complexity theory, Facebook, Heuristic algorithms, Time measurement, Engines

N. Kourtellis, G. De Francisci Morales and F. Bonchi, "Scalable online betweenness centrality in evolving graphs," 2016 IEEE 32nd International Conference on Data Engineering (ICDE), Helsinki, Finland, 2016, pp. 1580-1581.
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