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Preferential attachment when stable

Part of: Combinatorial probability Limit theorems

Published online by Cambridge University Press:  15 November 2019

Svante Janson ,
Subhabrata Sen  and
Joel Spencer
Svante Janson*
Affiliation:
Uppsala University
Subhabrata Sen*
Affiliation:
Massachusetts Institute of Technology
Joel Spencer*
Affiliation:
New York University
*
*Postal address: Department of Mathematics, Uppsala University, PO Box 480, SE-751 06 Uppsala, Sweden. Email address: svante.janson@math.uu.se
**Postal address: Department of Statistics, Harvard University, 1 Oxford Street, SC 712, Cambridge, MA 02138, USA. Email address: subhabratasen@fas.harvard.edu
**Postal address: Department of Statistics, Harvard University, 1 Oxford Street, SC 712, Cambridge, MA 02138, USA. Email address: subhabratasen@fas.harvard.edu
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Abstract

We study an urn process with two urns, initialized with a ball each. Balls are added sequentially, the urn being chosen independently with probability proportional to the $\alpha$th power $(\alpha >1)$ of the existing number of balls. We study the (rare) event that the urn compositions are balanced after the addition of $2n-2$ new balls. We derive precise asymptotics of the probability of this event by embedding the process in continuous time. Quite surprisingly, fine control of this probability may be leveraged to derive a lower-tail large deviation principle (LDP) for $L = \sum_{i=1}^{n} ({S_i^2}/{i^2})$, where $\{S_n \colon n \geq 0\}$ is a simple symmetric random walk started at zero. We provide an alternative proof of the LDP via coupling to Brownian motion, and subsequent derivation of the LDP for a continuous-time analog of L. Finally, we turn our attention back to the urn process conditioned to be balanced, and provide a functional limit law describing the trajectory of the urn process.

Keywords

Urn modellarge deviations

MSC classification

Primary: 60F10: Large deviations 60F17: Functional limit theorems; invariance principles
Secondary: 60C05: Combinatorial probability
Type
Original Article
Information
Advances in Applied Probability , Volume 51 , Issue 4 , December 2019 , pp. 1067 - 1108
Copyright
© Applied Probability Trust 2019 

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