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Structure of Business Firm Networks and Scale-Free Models

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  • Maksim Kitsak
  • Massimo Riccaboni
  • Shlomo Havlin
  • Fabio Pammolli
  • H. Eugene Stanley

Abstract

We study the structure of business firm networks in the Life Sciences (LS) and the Information and Communication Technology (ICT) sectors. We analyze business firm networks and scale-free models with degree distribution P(q) proportional to (q + c)^-λ using the method of k-shell decomposition. We find that the LS network consists of three components: a "nucleus", which is a small well connected subgraph, "tendrils", which are small subgraphs consisting of small degree nodes connected exclusively to the nucleus, and a "bulk body" which consists of the majority of nodes. At the same time we do not observe the above structure in the ICT network. Our results suggest that the sizes of the nucleus and the tendrils decrease as λ increases and disappear for λ greater or equal to 3. We compare the k-shell structure of random scale-free model networks with the real world business firm networks. The observed behavior of the k-shell structure in the two industries is consistent with a recently proposed growth model that assumes the coexistence of both preferential and random regimes in the evolution of industry networks.

Suggested Citation

  • Maksim Kitsak & Massimo Riccaboni & Shlomo Havlin & Fabio Pammolli & H. Eugene Stanley, 2008. "Structure of Business Firm Networks and Scale-Free Models," DISA Working Papers 0811, Department of Computer and Management Sciences, University of Trento, Italy, revised 16 Jan 2009.
    • Handle: RePEc:trt:disawp:0811
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    Cited by:

    1. Michael D. König & Xiaodong Liu & Yves Zenou, 2019. "R&D Networks: Theory, Empirics, and Policy Implications," The Review of Economics and Statistics, MIT Press, vol. 101(3), pages 476-491, July.
    2. Zhu, Zhen & Morrison, Greg & Puliga, Michelangelo & Chessa, Alessandro & Riccaboni, Massimo, 2018. "The similarity of global value chains: A network-based measure," Network Science, Cambridge University Press, vol. 6(4), pages 607-632, December.
    3. , D. & Tessone, Claudio J. & ,, 2014. "Nestedness in networks: A theoretical model and some applications," Theoretical Economics, Econometric Society, vol. 9(3), September.
    4. Zhen Zhu & Federica Cerina & Alessandro Chessa & Guido Caldarelli & Massimo Riccaboni, 2014. "The rise of China in the international trade network: a community core detection approach," Working Papers 4/2014, IMT School for Advanced Studies Lucca, revised Apr 2014.

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