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Clustering 1-Dimensional Periodic Network Using Betweenness Centrality

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Computational Social Networks (CSoNet 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9197))

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Abstract

In this paper, we propose a clustering method based on the infinite betweenness centrality for temporal networks specified by 1-dimensional periodic graphs. While the temporal networks have a wide range of applications such as opportunistic communication, there are not many clustering algorithms specifically proposed for them. We give a pseudo polynomial-time algorithm for temporal networks, of which the transit value is always positive and the least common divisor of all transit values is bounded. Our experimental results show that the centrality of networks with 125 nodes and 455 edges can be efficiently computed in 3.2 seconds. Not only the clustering results using the infinite betweenness centrality for this kind of networks are better, but also the nodes with biggest influence are more precisely detected when the betweenness centrality is computed over the periodic graph.

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Author information

Authors and Affiliations

  1. Global Research Center for Big Data Mathematics, National Institute of Informatics, Tokyo, Japan

    Norie Fu & Vorapong Suppakitpaisarn

  2. JST ERATO Kawarabayashi Large Graph Project, Tokyo, Japan

    Norie Fu & Vorapong Suppakitpaisarn

  3. Department of Computer Science, The University of Tokyo, Tokyo, Japan

    Vorapong Suppakitpaisarn

Authors
  1. Norie Fu
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  2. Vorapong Suppakitpaisarn
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Corresponding author

Correspondence to Vorapong Suppakitpaisarn .

Editor information

Editors and Affiliations

  1. Computer and Information Science and Engineering, University of Florida, Gainesville, Florida, USA

    My T. Thai

  2. Towsen University, Towson, Maryland, USA

    Nam P. Nguyen

  3. Chinese Academy of Sciences, Beijing, China

    Huawei Shen

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© 2015 Springer International Publishing Switzerland

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Cite this paper

Fu, N., Suppakitpaisarn, V. (2015). Clustering 1-Dimensional Periodic Network Using Betweenness Centrality. In: Thai, M., Nguyen, N., Shen, H. (eds) Computational Social Networks. CSoNet 2015. Lecture Notes in Computer Science(), vol 9197. Springer, Cham. https://doi.org/10.1007/978-3-319-21786-4_11

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  • DOI: https://doi.org/10.1007/978-3-319-21786-4_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21785-7

  • Online ISBN: 978-3-319-21786-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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