Glauber dynamics for Ising models on random regular graphs: cut-off and metastability

Van Hao Can*, Remco van der Hofstad, Takashi Kumagai

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Consider random d-regular graphs, i.e., random graphs such that there are exactly d edges from each vertex for some d ≥ 3. We study both the configuration model version of this graph, which has occasional multi-edges and self-loops, as well as the simple version of it, which is a d-regular graph chosen uniformly at random from the collection of all d-regular graphs. In this paper, we discuss mixing times of Glauber dynamics for the Ising model with an external magnetic field on a random d-regular graph, both in the quenched as well as the annealed settings. Let ß be the inverse temperature, ßc be the critical temperature and B be the external magnetic field. Concerning the annealed measure, we show that for ß > ßc there exists Bc(ß) ϵ (0, ∞) such that the model is metastable (i.e., the mixing time is exponential in the graph size n) when ß > ßc and 0 ≤ B < Bc(ß), whereas it exhibits the cut-off phenomenon at c*n logn with a window of order n when ß < ßc or ß > ßc and B > Bc(ß). Interestingly, Bc(ß) coincides with the critical external field of the Ising model on the d-ary tree (namely, above which the model has a unique Gibbs measure). Concerning the quenched measure, we show that there exists Bc(ß) with Bc(ß) ≤ Bc(ß) such that for ß > ßc, the mixing time is at least exponential along some subsequence (nk)k≥1 when 0 ≤ B < Bc(ß), whereas it is less than or equal to Cnlogn when B > Bc(ß). The quenched results also hold for the model conditioned on simplicity, for the annealed results this is unclear.

Original languageEnglish
Pages (from-to)1441-1482
Number of pages42
Issue number1
Publication statusPublished - 2021
Externally publishedYes


  • Cut-off
  • Glauber dynamics
  • Ising model
  • Metastability
  • Random regular graphs

ASJC Scopus subject areas

  • Statistics and Probability


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