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A positive minimum degree envelope at every fixed supercritical ratio

Statement

Fix γ0>1. There are constants a∈(0,1/2), b=4/log⁡2>1 and η>0, depending only on γ0, such that for all sufficiently large positive integers n and all p∈[0,1] with d=np≥γ0log⁡n, a random graph G∼G(n,p) on [n], whose unordered pairs of distinct vertices have mutually independent Bernoulli(p) edge indicators, satisfies P(some vertex of G has degree outside [ad,bd])≤n−η+n−2. The cutoff is uniform over those p.

Facts & Assumptions

[F1]

The graph G∼G(n,p) has one mutually independent Bernoulli(p) edge indicator for each unordered pair of distinct vertices, on the finite product probability space of The Erdős-Rényi finite random graph G(n,p). The Bernoulli and binomial laws use Bernoulli random variables and binomial random variables as sums of independent Bernoulli trials. Each degree has law Bin⁡(n−1,p) by Single-vertex degree laws and the shared-edge dependence, and therefore mean μ=(n−1)p by A Bernoulli(p) variable has mean p and variance p(1-p); a binomial(n,p) variable has mean np and variance np(1-p). Markov's inequality, product factorization of expectation and the union bound are Markov's inequality on a finite probability space, Expectation factors over a finite product of mutually independent random variables and The finite union bound.

[F3]

For a finite family of finite-valued random variables, mutual independence means that probabilities of prescribed attained values factor over every nonempty subfamily (Pairwise and mutual independence of finite-valued random variables); the empty family is independent.

Proof

Given: A fixed γ0>1, a positive integer n, and the random graph G∼G(n,p) with the parameters and independent edge indicators in the Statement.

Goal: Choose constants and establish the simultaneous envelope uniformly.

1.1F1F2F3

Write a degree as S=∑i=1n−1Xi using its incident edge indicators. For fixed real t≠0, the map x↦etx is injective on {0,1} by [F2]; every joint atom for any subfamily of (etXi)i is therefore the corresponding joint atom for (Xi)i. Its probability factors by [F3], proving mutual independence of the transformed variables. For t=0 they are all constant 1, so their joint atom probabilities also factor. All factors are finite-valued on the finite graph probability space, so their expectations are finite. Product factorization in [F1] and the exponential addition formula in [F2] now give EetS=(1+p(et−1))n−1≤exp⁡(μ(et−1)). The elementary bound 1+x≤ex follows from the derivative of ex−1−x, whose minimum is at zero. For 0<r<1, apply Markov to rS on S≤rμ and obtain P(S≤rμ)≤exp⁡(−μh(r)),h(r)=1−r+rlog⁡r. Indeed ErS≤eμ(r−1) and the threshold is rrμ.

2.1F1F2step 1.1

As r↓0, rlog⁡r→0 by substituting t=1/r in [F2], so h(r)→1. Choose r∈(0,1) with γ0h(r)>1 and set a=r/2, η=(γ0h(r)−1)/2>0. For n≥2, ad≤rμ; eventually (1−1/n)γ0h(r)≥1+η. A union bound then gives P(∃v:Sv<ad)≤ne−μh(r)≤n−η. The cutoff depends on γ0,r only, since μ≥(1−1/n)γ0log⁡n.

3.1F1F2step 1.1step 2.1∎

Take t=log⁡2>0, b=4/log⁡2. The same moment bound gives P(S≥bd)≤eμ−bdlog⁡2≤e−3d. The union probability for Sv>bd is therefore at most ne−3d≤n1−3γ0≤n−2. Positivity of b−1 follows from 0<log⁡2<1, for example by integrating 1/x from 1 to 2. Add the two exceptional probabilities. No independence between different degrees is used.

Depends on

Used by · 1

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