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International Workshop on Frontiers in Algorithmics

FAW 2008: Frontiers in Algorithmics pp 186–195Cite as

Ranking of Closeness Centrality for Large-Scale Social Networks

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5059))

Abstract

Closeness centrality is an important concept in social network analysis. In a graph representing a social network, closeness centrality measures how close a vertex is to all other vertices in the graph. In this paper, we combine existing methods on calculating exact values and approximate values of closeness centrality and present new algorithms to rank the top-k vertices with the highest closeness centrality. We show that under certain conditions, our algorithm is more efficient than the algorithm that calculates the closeness-centralities of all vertices.

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Author information

Authors and Affiliations

  1. Kyoto University,  

    Kazuya Okamoto

  2. Microsoft Research Asia,  

    Wei Chen

  3. Illinois Institute of Technology and Microsoft Research Asia,  

    Xiang-Yang Li

Authors
  1. Kazuya Okamoto
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  2. Wei Chen
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  3. Xiang-Yang Li
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Editor information

Franco P. Preparata Xiaodong Wu Jianping Yin

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© 2008 Springer-Verlag Berlin Heidelberg

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Okamoto, K., Chen, W., Li, XY. (2008). Ranking of Closeness Centrality for Large-Scale Social Networks. In: Preparata, F.P., Wu, X., Yin, J. (eds) Frontiers in Algorithmics. FAW 2008. Lecture Notes in Computer Science, vol 5059. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69311-6_21

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  • DOI: https://doi.org/10.1007/978-3-540-69311-6_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69310-9

  • Online ISBN: 978-3-540-69311-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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