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Fully-dynamic Approximation of Betweenness Centrality. [Preprint]

Bergamini, Elisabetta; Meyerhenke, Henning

Abstract:
Betweenness is a well-known centrality measure that ranks the nodes of a network according to their participation in shortest paths. Since an exact computation is prohibitive in large networks, several approximation algorithms have been proposed. Besides that, recent years have seen the publication of dynamic algorithms for efficient recomputation of betweenness in evolving networks. In previous work we proposed the first semi-dynamic algorithms that recompute an approximation of betweenness in connected graphs after batches of edge insertions.
In this paper we propose the first fully-dynamic approximation algorithms (for weighted and unweighted undirected graphs that need not to be connected) with a provable guarantee on the maximum approximation error. The transfer to fully-dynamic and disconnected graphs implies additional algorithmic problems that could be of independent interest. In particular, we propose a new upper bound on the vertex diameter for weighted undirected graphs. For both weighted and unweighted graphs, we also propose the first fully-dynamic algorithms that keep track of such upper bound. In addition, we extend ... mehr

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Seitenaufrufe: 10
seit 06.05.2018
Zugehörige Institution(en) am KIT Institut für Theoretische Informatik (ITI)
Publikationstyp Buchaufsatz
Jahr 2015
Sprache Englisch
Identifikator KITopen-ID: 1000055805
Erschienen in arXiv [cs.DS] : Data Structures and Algorithms
Seiten arXiv:1504.07091
Bemerkung zur Veröffentlichung arCiv.org
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