We study the convergence to stationarity for random walks on dynamic random digraphs with given degree sequences. The digraphs undergo full regeneration at independent geometrically distributed random time intervals with parameter α. Relaxation to stationarity is the result of an interplay of regeneration and mixing on the static digraph. When the number of vertices n tends to infinity and the parameter α tends to zero, we find three scenarios according to whether αlogn converges to zero, infinity or to some finite positive value: when the limit is zero, relaxation to stationarity occurs in two separate stages, the first due to mixing on the static digraph, and the second due to regeneration; when the limit is infinite, there is not enough time for the static digraph to mix and the relaxation to stationarity is dictated by the regeneration only; finally, when the limit is a finite positive value we find a mixed behavior interpolating between the two extremes. A crucial ingredient of our analysis is the control of suitable approximations for the unknown stationary distribution.

Caputo, P., Quattropani, M. (2021). Mixing time trichotomy in regenerating dynamic digraphs. STOCHASTIC PROCESSES AND THEIR APPLICATIONS, 137, 222-251 [10.1016/j.spa.2021.03.003].

Mixing time trichotomy in regenerating dynamic digraphs

Caputo P.;Quattropani M.
2021-01-01

Abstract

We study the convergence to stationarity for random walks on dynamic random digraphs with given degree sequences. The digraphs undergo full regeneration at independent geometrically distributed random time intervals with parameter α. Relaxation to stationarity is the result of an interplay of regeneration and mixing on the static digraph. When the number of vertices n tends to infinity and the parameter α tends to zero, we find three scenarios according to whether αlogn converges to zero, infinity or to some finite positive value: when the limit is zero, relaxation to stationarity occurs in two separate stages, the first due to mixing on the static digraph, and the second due to regeneration; when the limit is infinite, there is not enough time for the static digraph to mix and the relaxation to stationarity is dictated by the regeneration only; finally, when the limit is a finite positive value we find a mixed behavior interpolating between the two extremes. A crucial ingredient of our analysis is the control of suitable approximations for the unknown stationary distribution.
Caputo, P., Quattropani, M. (2021). Mixing time trichotomy in regenerating dynamic digraphs. STOCHASTIC PROCESSES AND THEIR APPLICATIONS, 137, 222-251 [10.1016/j.spa.2021.03.003].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/401612
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