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A positive minimum degree envelope at every fixed supercritical ratio
Statement
Fix . There are constants , and , depending only on , such that for all sufficiently large positive integers and all with , a random graph on , whose unordered pairs of distinct vertices have mutually independent Bernoulli edge indicators, satisfies The cutoff is uniform over those .
Statement references
The Erdős-Rényi finite random graph G(n,p)
Single-vertex degree laws and the shared-edge dependence
The natural logarithm as the inverse of the exponential function
Real powers for positive bases, with the zero-base positive-exponent convention
Bernoulli random variables and binomial random variables as sums of independent Bernoulli trials
Facts & Assumptions
The graph has one mutually independent Bernoulli 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 by Single-vertex degree laws and the shared-edge dependence, and therefore mean 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.
The exponential and logarithm have their ordinary inverse identities (The natural logarithm as the inverse of the exponential function, The real exponential function and the number e by a power series). Positive-base real powers mean for and (Real powers for positive bases, with the zero-base positive-exponent convention). Their derivative, monotonicity and product/integral identities follow from The exponential addition formula exp(x+y)=exp(x)exp(y), The exponential function is smooth and (exp)'=exp, The exponential function is strictly increasing and The natural logarithm has derivative 1/x and equals the integral from 1 to x of 1/t. The limit is supplied by The logarithm grows more slowly than every positive real power.
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 , a positive integer , and the random graph with the parameters and independent edge indicators in the Statement.
Goal: Choose constants and establish the simultaneous envelope uniformly.
Write a degree as using its incident edge indicators. For fixed real , the map is injective on by [F2]; every joint atom for any subfamily of is therefore the corresponding joint atom for . Its probability factors by [F3], proving mutual independence of the transformed variables. For they are all constant , 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 . The elementary bound follows from the derivative of , whose minimum is at zero. For , apply Markov to on and obtain Indeed and the threshold is .
As , by substituting in [F2], so . Choose with and set , . For , ; eventually . A union bound then gives The cutoff depends on only, since .
Take , . The same moment bound gives . The union probability for is therefore at most . Positivity of follows from , for example by integrating from 1 to 2. Add the two exceptional probabilities. No independence between different degrees is used.
Depends on
- Single-vertex degree laws and the shared-edge dependence
- Markov's inequality on a finite probability space
- Expectation factors over a finite product of mutually independent random variables
- The finite union bound
- The natural logarithm as the inverse of the exponential function
- The real exponential function and the number e by a power series
- The logarithm grows more slowly than every positive real power
- The exponential addition formula exp(x+y)=exp(x)exp(y)
- The exponential function is smooth and (exp)'=exp
- The exponential function is strictly increasing
- The natural logarithm has derivative 1/x and equals the integral from 1 to x of 1/t
- The Erdős-Rényi finite random graph G(n,p)
- 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)
- Real powers for positive bases, with the zero-base positive-exponent convention
- Pairwise and mutual independence of finite-valued random variables
- Bernoulli random variables and binomial random variables as sums of independent Bernoulli trials
Dependency tree · 2 levels
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