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Entropy of Dynamical Social Networks

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  • Kun Zhao
  • Márton Karsai
  • Ginestra Bianconi

Abstract

Human dynamical social networks encode information and are highly adaptive. To characterize the information encoded in the fast dynamics of social interactions, here we introduce the entropy of dynamical social networks. By analysing a large dataset of phone-call interactions we show evidence that the dynamical social network has an entropy that depends on the time of the day in a typical week-day. Moreover we show evidence for adaptability of human social behavior showing data on duration of phone-call interactions that significantly deviates from the statistics of duration of face-to-face interactions. This adaptability of behavior corresponds to a different information content of the dynamics of social human interactions. We quantify this information by the use of the entropy of dynamical networks on realistic models of social interactions.

Suggested Citation

  • Kun Zhao & Márton Karsai & Ginestra Bianconi, 2011. "Entropy of Dynamical Social Networks," PLOS ONE, Public Library of Science, vol. 6(12), pages 1-7, December.
    • Handle: RePEc:plo:pone00:0028116
    DOI: 10.1371/journal.pone.0028116
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    References listed on IDEAS

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    1. Dorogovtsev, S.N. & Mendes, J.F.F., 2003. "Evolution of Networks: From Biological Nets to the Internet and WWW," OUP Catalogue, Oxford University Press, number 9780198515906.
    2. Jon M. Kleinberg, 2000. "Navigation in a small world," Nature, Nature, vol. 406(6798), pages 845-845, August.
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    Cited by:

    1. Carlos F Alvarez & Luis E Palafox & Leocundo Aguilar & Mauricio A Sanchez & Luis G Martinez, 2016. "Using Link Disconnection Entropy Disorder to Detect Fast Moving Nodes in MANETs," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-15, May.

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