On the method of typical bounded differences

Concentration inequalities are fundamental tools in probabilistic combinatorics and theoretical computer science for proving that random functions are near their means. Of particular importance is the case where f(X) is a function of independent random variables X=(X_1, ..., X_n). Here the well known bounded differences inequality (also called McDiarmid's or Hoeffding-Azuma inequality) establishes sharp concentration if the function f does not depend too much on any of the variables. One attractive feature is that it relies on a very simple Lipschitz condition (L): it suffices to show that |f(X)-f(X')| \leq c_k whenever X,X' differ only in X_k. While this is easy to check, the main disadvantage is that it considers worst-case changes c_k, which often makes the resulting bounds too weak to be useful. In this paper we prove a variant of the bounded differences inequality which can be used to establish concentration of functions f(X) where (i) the typical changes are small although (ii) the worst case changes might be very large. One key aspect of this inequality is that it relies on a simple condition that (a) is easy to check and (b) coincides with heuristic considerations why concentration should hold. Indeed, given an event \Gamma that holds with very high probability, we essentially relax the Lipschitz condition (L) to situations where \Gamma occurs. The point is that the resulting typical changes c_k are often much smaller than the worst case ones. To illustrate its application we consider the reverse H-free process, where H is 2-balanced. We prove that the final number of edges in this process is concentrated, and also determine its likely value up to constant factors. This answers a question of Bollob\'as and Erd\H{o}s.Comment: 25 page

dd-connectivity of the random graph with restricted budget

In this short note, we consider a graph process recently introduced by Frieze, Krivelevich and Michaeli. In their model, the edges of the complete graph $K_n$ are ordered uniformly at random and are then revealed consecutively to a player called Builder. At every round, Builder must decide if they accept the edge proposed at this round or not. We prove that, for every $d\ge 2$, Builder can construct a spanning $d$-connected graph after $(1+o(1))n\log n/2$ rounds by accepting $(1+o(1))dn/2$ edges with probability converging to 1 as $n\to \infty$. This settles a conjecture of Frieze, Krivelevich and Michaeli.Comment: 15 pages, 2 figures, revised versio

A scaling limit for the length of the longest cycle in a sparse random graph

We discuss the length L-c,L-n of the longest cycle in a sparse random graph G(n,p), p = c/n, c constant. We show that for large c there exists a function f (c) such that L-c,L-n/n -> f (c) a.s. The function f (c) = 1 - Sigma(infinity)(k=1) p(k)(c)e(-kc) where pk(c) is a polynomial in c. We are only able to explicitly give the values p(1), p(2), although we could in principle compute any p(k). We see immediately that the length of the longest path is also asymptotic to f (c)n w.h.p