Percolation and Random Graphs

In this chapter, we define percolation and random graph models, and survey the features of these model

Random graph asymptotics on high-dimensional tori II. Volume, diameter and mixing time

For critical bond-percolation on high-dimensional torus, this paper proves sharp lower bounds on the size of the largest cluster, removing a logarithmic correction in the lower bound in Heydenreich and van der Hofstad (Comm Math Phys 270(2):335–358, 2007). This improvement finally settles a conjecture by Aizenman (Nuclear Phys B 485(3):551–582, 1997) about the role of boundary conditions in critical high-dimensional percolation, and it is a key step in deriving further properties of critical percolation on the torus. Indeed, a criterion of Nachmias and Peres (Ann Probab 36(4):1267–1286, 2008) implies appropriate bounds on diameter and mixing time of the largest clusters. We further prove that the volume bounds apply also to any finite number of the largest clusters. Finally, we show that any weak limit of the largest connected component is non-degenerate, which can be viewed as a significant sign of critical behavior. The main conclusion of the paper is that the behavior of critical percolation on the high-dimensional torus is the same as for critical Erdos-Rényi random graphs. Keywords: Percolation – Random graph asymptotics – Mean-field behavior – Critical windo

An expansion for self-interacting random walks

We derive a perturbation expansion for general self-interacting random walks, where steps are made on the basis of the history of the path. Examples of models where this expansion applies are reinforced random walk, excited random walk, the true (weakly) self-avoiding walk, loop-erased random walk, and annealed random walk in random environment. In this paper we show that the expansion gives rise to useful formulae for the speed and variance of the random walk, when these quantities are known to exist. The results and formulae of this paper have been used elsewhere by the authors to prove monotonicity properties for the speed (in high dimensions) of excited random walk and related models, and certain models of random walk in random environment. We also derive a law of large numbers and central limit theorem (with explicit error terms) directly from this expansion, under strong assumptions on the expansion coefficients. The assumptions are shown to be satisfied by excited random walk in high dimensions with small excitation parameter, a model of reinforced random walk with underlying drift and small reinforcement parameter, and certain models of random walk in random environment under strong ellipticity conditions

Diameter of the stochastic mean-field model of distance

We consider the complete graph K_n on n vertices with exponential mean n edge lengths. Writing C_{ij} for the weight of the smallest-weight path between vertex i,j \in [n], Janson [17] showed that max_{i,j \in [n]} C_{ij} / log n converges in probability to 3. We extend this results by showing that max_{i,j \in [n]} C_{ij} - 3 log n converges in distribution to some limiting random variable that can be identified via a maximization procedure on a limiting infinite random structure. Interestingly, this limiting random variable has also appeared as the weak limit of the re-centered graph diameter of the barely supercritical Erdös-Rényi random graph in [21]

Universality for first passage percolation on sparse uniform and rank-1 random graphs

In [3], we considered first passage percolation on the configuration model equipped with general independent and identically distributed edge weights, where the common distribution function admits a density. Assuming that the degree distribution satisfies a uniform X^2 log X - condition, we analyzed the asymptotic distribution for the minimal weight path between a pair of typical vertices, as well as the asymptotic distribution of the number of edges on this path. Given the interest in understanding such questions for various other random graph models, the aim of this paper is to show how these results extend to uniform random graphs with a given degree sequence and rank-one inhomogeneous random graphs

Optimal subgraph structures in scale-free configuration models

Subgraphs reveal information about the geometry and functionalities of complex networks. For scale-free networks with unbounded degree fluctuations, we count the number of times a small connected graph occurs as a subgraph (motif counting) or as an induced subgraph (graphlet counting). We obtain these results by analyzing the configuration model with degree exponent $\tau\in(2,3)$ and introducing a novel class of optimization problems. For any given subgraph, the unique optimizer describes the degrees of the nodes that together span the subgraph. We find that every subgraph occurs typically between vertices with specific degree ranges. In this way, we can count and characterize {\it all} subgraphs. We refrain from double counting in the case of multi-edges, essentially counting the subgraphs in the {\it erased} configuration model

Universality for first passage percolation on sparse random graphs

We consider first passage percolation on the conguration model with n vertices, and general independent and identically distributed edge weights assumed to have a density. Assuming that the degree distribution satisfies a uniform X2 logX-condition, we analyze the asymptotic distribution for the minimal weight path between a pair of typical vertices, as well the number of edges on this path namely the hopcount. The hopcount satisfies a central limit theorem (CLT). Furthermore, writing Ln for the weight of this optimal path, then we shown that Ln(log n)= n converges to a limiting random variable, for some sequence n. This sequence n and the norming constants for the CLT are expressible in terms of the parameters of an associated continuous-time branching process that describes the growth of neighborhoods around a uniformly chosen vertex in the random graph. The limit of Ln(log n)= n equals the sum of the logarithm of the product of two independent martingale limits, and a Gumbel random variable. Till date, for sparse random graph models, such results have been shown only for the special case where the edge weights have an exponential distribution, wherein the Markov property of this distribution plays a crucial role in the technical analysis of the problem. The proofs in the paper rely on a refined coupling between shortest path trees and continuous- time branching processes, and on a Poisson point process limit for the potential closing edges of shortest-weight paths between the source and destination