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Hierarchical social networks and information flow

Author

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  • López, Luis
  • F.F. Mendes, Jose
  • Sanjuán, Miguel A.F

Abstract

Using a simple model for the information flow on social networks, we show that the traditional hierarchical topologies frequently used by companies and organizations, are poorly designed in terms of efficiency. Moreover, we prove that this type of structures are the result of the individual aim of monopolizing as much information as possible within the network. As the information is an appropriate measurement of centrality, we conclude that this kind of topology is so attractive for leaders, because the global influence each actor has within the network is completely determined by the hierarchical level occupied.

Suggested Citation

  • López, Luis & F.F. Mendes, Jose & Sanjuán, Miguel A.F, 2002. "Hierarchical social networks and information flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 695-708.
    • Handle: RePEc:eee:phsmap:v:316:y:2002:i:1:p:695-708
    DOI: 10.1016/S0378-4371(02)01333-X
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    References listed on IDEAS

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    1. Barabási, Albert-László & Albert, Réka & Jeong, Hawoong, 2000. "Scale-free characteristics of random networks: the topology of the world-wide web," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 281(1), pages 69-77.
    2. Emily M. Jin & Michelle Girvan & M. E. J. Newman, 2001. "The Structure of Growing Social Networks," Working Papers 01-06-032, Santa Fe Institute.
    3. Kirman, Alan P & Oddou, Claude & Weber, Shlomo, 1986. "Stochastic Communication and Coalition Formation," Econometrica, Econometric Society, vol. 54(1), pages 129-138, January.
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    Cited by:

    1. Almendral, Juan A. & López, Luis & Cholvi, Vicent & Sanjuán, Miguel A.F., 2005. "Congestion schemes and Nash equilibrium in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 355(2), pages 602-618.
    2. Ehsani, Maryam & Makui, Ahmad & Sadi Nezhad, Soheil, 2010. "A methodology for analyzing decision networks, based on information theory," European Journal of Operational Research, Elsevier, vol. 202(3), pages 853-863, May.

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