scholarly journals Phase transition for the vacant set left by random walk on the giant component of a random graph

2015 ◽  
Vol 51 (2) ◽  
pp. 756-780 ◽  
Author(s):  
Tobias Wassmer
Keyword(s):  
Phase Transition ◽  
Random Walk ◽  
Random Graph ◽  
Giant Component

scholarly journals The Phase Transition in the Configuration Model

2012 ◽  
Vol 21 (1-2) ◽  
pp. 265-299 ◽  
Author(s):  
OLIVER RIORDAN
Keyword(s):  
Phase Transition ◽  
Random Graph ◽  
Regular Graph ◽  
Degree Sequence ◽  
Natural Extension ◽  
Giant Component ◽  
Configuration Model ◽  
Random Subgraph ◽  
Single Method ◽  
Present Setting

Let G = G(d) be a random graph with a given degree sequence d, such as a random r-regular graph where r ≥ 3 is fixed and n = |G| → ∞. We study the percolation phase transition on such graphs G, i.e., the emergence as p increases of a unique giant component in the random subgraph G[p] obtained by keeping edges independently with probability p. More generally, we study the emergence of a giant component in G(d) itself as d varies. We show that a single method can be used to prove very precise results below, inside and above the ‘scaling window’ of the phase transition, matching many of the known results for the much simpler model G(n, p). This method is a natural extension of that used by Bollobás and the author to study G(n, p), itself based on work of Aldous and of Nachmias and Peres; the calculations are significantly more involved in the present setting.

scholarly journals A Simple Branching Process Approach to the Phase Transition in $G_{n,p}$

10.37236/2588 ◽  
2012 ◽  
Vol 19 (4) ◽  
Author(s):  
Béla Bollobás ◽  
Oliver Riordan
Keyword(s):  
Phase Transition ◽  
Random Graph ◽  
Branching Process ◽  
Process Approach ◽  
Giant Component ◽  
Asymptotic Size ◽  
The Way

It is well known that the branching process approach to the study of the random graph $G_{n,p}$ gives a very simple way of understanding the size of the giant component when it is fairly large (of order $\Theta(n)$). Here we show that a variant of this approach works all the way down to the phase transition: we use branching process arguments to give a simple new derivation of the asymptotic size of the largest component whenever $(np-1)^3n\to\infty$.

Random graphs

2018 ◽  
Author(s):  
Mark Newman
Keyword(s):  
Random Graph ◽  
Random Graphs ◽  
Large Scale ◽  
Random Network ◽  
Graph Model ◽  
Clustering Coefficient ◽  
Giant Component ◽  
Random Graph Model ◽  
Definition Of ◽  
Average Size

An introduction to the mathematics of the Poisson random graph, the simplest model of a random network. The chapter starts with a definition of the model, followed by derivations of basic properties like the mean degree, degree distribution, and clustering coefficient. This is followed with a detailed derivation of the large-scale structural properties of random graphs, including the position of the phase transition at which a giant component appears, the size of the giant component, the average size of the small components, and the expected diameter of the network. The chapter ends with a discussion of some of the shortcomings of the random graph model.

The cover time of the giant component of a random graph

2008 ◽  
Vol 32 (4) ◽  
pp. 401-439 ◽  
Author(s):  
Colin Cooper ◽  
Alan Frieze
Keyword(s):  
Random Graph ◽  
Giant Component ◽  
Cover Time

scholarly journals A phase transition in the random transposition random walk

2005 ◽  
Vol 136 (2) ◽  
pp. 203-233 ◽  
Author(s):  
Nathanaël Berestycki ◽  
Rick Durrett
Keyword(s):  
Phase Transition ◽  
Random Walk ◽  
Random Transposition

Random Graphs from a Minor-Closed Class

2009 ◽  
Vol 18 (4) ◽  
pp. 583-599 ◽  
Author(s):  
COLIN McDIARMID
Keyword(s):  
Poisson Distribution ◽  
Random Graph ◽  
Generating Function ◽  
Random Graphs ◽  
Giant Component ◽  
Closed Class ◽  
Exponential Generating Function ◽  
Excluded Minor ◽  
A Minor ◽  
Labelled Graphs

A minor-closed class of graphs is addable if each excluded minor is 2-connected. We see that such a classof labelled graphs has smooth growth; and, for the random graphRnsampled uniformly from then-vertex graphs in, the fragment not in the giant component asymptotically has a simple ‘Boltzmann Poisson distribution’. In particular, asn→ ∞ the probability thatRnis connected tends to 1/A(ρ), whereA(x) is the exponential generating function forand ρ is its radius of convergence.

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