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Are Randomly Grown Graphs Really Random?

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  • D. S. Callaway
  • J. E. Hopcroft
  • J. M. Kleinberg
  • M. E. J. Newman
  • S. H. Strogatz
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    Abstract

    We analyze a minimal model of a growing network. At each time step, a new vertex is added; then, with probability \delta, two vertices are chosen uniformly at random and joined by an undirected edge. This process is repeated for t time steps. In the limit of large t, the resulting graph displays surprisingly rich characteristics. In particular, a giant component emerges in an infinite-order phase transition at \delta = 1/8. At the transition, the average component size jumps discontinuously but remains finite. In contrast, a static random graph with the same degree distribution exhibits a second-order phase transition at \delta = 1/4, and the average component size diverges there. These dramatic differences between grown and static random graphs stem from a positive correlation between the degrees of connected vertices in the grown graph--older vertices tend to have higher degree, and to link with other high-degree vertices, merely by virtue of their age. We conclude that grown graphs, however randomly they are constructed, are fundamentally different from their static random graph counterparts.

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    Bibliographic Info

    Paper provided by Santa Fe Institute in its series Working Papers with number 01-05-025.

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    Date of creation: May 2001
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    Handle: RePEc:wop:safiwp:01-05-025

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    Cited by:
    1. Konno, Tomohiko, 2009. "Network structure of Japanese firms. Scale-free, hierarchy, and degree correlation: analysis from 800,000 firms," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy, vol. 3(31), pages 1-13.
    2. Morehead, Raymond & Noore, Afzel, 2007. "Novel hybrid mitigation strategy for improving the resiliency of hierarchical networks subjected to attacks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 378(2), pages 603-612.
    3. Brot, Hilla & Muchnik, Lev & Goldenberg, Jacob & Louzoun, Yoram, 2012. "Feedback between node and network dynamics can produce real-world network properties," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(24), pages 6645-6654.

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