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Fuzzy Centrality Evaluation in Complex and Multiplex Networks

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Complex Networks VIII (CompleNet 2017)

Part of the book series: Springer Proceedings in Complexity ((SPCOM))

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Abstract

Centrality rankings are classically used to analyze the influence of nodes in different types of networks. However, since most centrality indices are very sensitive to missing or additional edges and since most complex networks are based on faulty data, a precise ranking is quite unlikely to be obtained. Thus, in this paper we propose to use an assignment of the nodes to a predefined and small set of centrality classes using a fuzzy model, ranging from “very peripheral” to “very central”. We show empirically that the assignment of nodes to these classes is quite robust against random noise. Furthermore, the method can also be used to combine possibly conflicting classes of the nodes based on different centrality values over multiple networks using a fuzzy operator.

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Author information

Authors and Affiliations

  1. Graph Theory and Complex Network Analysis Group, Computer Science Department, Kaiserslautern University of Technology, Gottlieb-Daimler-Str. 48, 67663, Kaiserslautern, Germany

    Sude Tavassoli & Katharina A. Zweig

Authors
  1. Sude Tavassoli
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  2. Katharina A. Zweig
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Corresponding author

Correspondence to Sude Tavassoli .

Editor information

Editors and Affiliations

  1. New York University, Center for Data Science New York University, New York, New York, USA

    Bruno Gonçalves

  2. BioComplex Laboratory, School of Computing, Florida Institute of Technology, Melbourne, Florida, USA

    Ronaldo Menezes

  3. Center for Network Science and Mathematics Department, Central European University, Budapest, Hungary

    Roberta Sinatra

  4. Rudjer Bošković Institute, Theoretical Physics Division, Zagreb, Croatia

    Vinko Zlatic

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Tavassoli, S., Zweig, K.A. (2017). Fuzzy Centrality Evaluation in Complex and Multiplex Networks. In: Gonçalves, B., Menezes, R., Sinatra, R., Zlatic, V. (eds) Complex Networks VIII. CompleNet 2017. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-54241-6_3

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