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Transient and slim versus recurrent and fat: Random walks and the trees they grow

  • Giulio Iacobelli (a1), Daniel R. Figueiredo (a1) and Giovanni Neglia (a2)

Abstract

The no restart random walk (NRRW) is a random network growth model driven by a random walk that builds the graph while moving on it, adding and connecting a new leaf node to the current position of the walker every s steps. We show a fundamental dichotomy in NRRW with respect to the parity of s: for ${s}=1$ we prove that the random walk is transient and non-leaf nodes have degrees bounded above by an exponential distribution; for s even we prove that the random walk is recurrent and non-leaf nodes have degrees bounded below by a power law distribution. These theoretical findings highlight and confirm the diverse and rich behaviour of NRRW observed empirically.

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Corresponding author

* Postal address: Instituto de Matemática, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil. Email address giulio@im.ufrj.br
** Postal address: Department of Computer and System Engineering, COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil. Email address daniel@cos.ufrj.br
*** Postal address: NEO Team, Inria, Sophia-Antipolis, France.Email address giovanni.neglia@inria.fr

References

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Transient and slim versus recurrent and fat: Random walks and the trees they grow

  • Giulio Iacobelli (a1), Daniel R. Figueiredo (a1) and Giovanni Neglia (a2)

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