Premium
Critical percolation on random regular graphs
Author(s) -
Nachmias Asaf,
Peres Yuval
Publication year - 2010
Publication title -
random structures and algorithms
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.314
H-Index - 69
eISSN - 1098-2418
pISSN - 1042-9832
DOI - 10.1002/rsa.20277
Subject(s) - random graph , mathematics , limiting , brownian motion , combinatorics , excursion , percolation (cognitive psychology) , percolation threshold , graph , component (thermodynamics) , subadditivity , statistical physics , discrete mathematics , physics , statistics , thermodynamics , quantum mechanics , law , mechanical engineering , neuroscience , political science , engineering , biology , electrical resistivity and conductivity
The behavior of the random graph G ( n , p ) around the critical probability p c = $ {1 \over n} $ is well understood. When p = (1 + O ( n 1/3 )) p c the components are roughly of size n 2/3 and converge, when scaled by n −2/3 , to excursion lengths of a Brownian motion with parabolic drift. In particular, in this regime, they are not concentrated. When p = (1 ‐ ϵ( n )) p c with ϵ( n ) n 1/3 →∞ (the subcritical regime) the largest component is concentrated around 2ϵ −2 log(ϵ 3 n ). When p = (1 + ϵ( n )) p c with ϵ( n ) n 1/3 →∞ (the supercritical regime), the largest component is concentrated around 2ϵ n and a duality principle holds: other component sizes are distributed as in the subcritical regime. Itai Benjamini asked whether the same phenomenon occurs in a random d ‐regular graph. Some results in this direction were obtained by (Pittel, Ann probab 36 (2008) 1359–1389). In this work, we give a complete affirmative answer, showing that the same limiting behavior (with suitable d dependent factors in the non‐critical regimes) extends to random d ‐regular graphs. © 2009 Wiley Periodicals, Inc. Random Struct. Alg., 2010
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom