30,977 research outputs found
Uniform generation of random graphs with power-law degree sequences
We give a linear-time algorithm that approximately uniformly generates a
random simple graph with a power-law degree sequence whose exponent is at least
2.8811. While sampling graphs with power-law degree sequence of exponent at
least 3 is fairly easy, and many samplers work efficiently in this case, the
problem becomes dramatically more difficult when the exponent drops below 3;
ours is the first provably practicable sampler for this case. We also show that
with an appropriate rejection scheme, our algorithm can be tuned into an exact
uniform sampler. The running time of the exact sampler is O(n^{2.107}) with
high probability, and O(n^{4.081}) in expectation.Comment: 50 page
Superdiffusion in a class of networks with marginal long-range connections
A class of cubic networks composed of a regular one-dimensional lattice and a
set of long-range links is introduced. Networks parametrized by a positive
integer k are constructed by starting from a one-dimensional lattice and
iteratively connecting each site of degree 2 with a th neighboring site of
degree 2. Specifying the way pairs of sites to be connected are selected,
various random and regular networks are defined, all of which have a power-law
edge-length distribution of the form with the marginal
exponent s=1. In all these networks, lengths of shortest paths grow as a power
of the distance and random walk is super-diffusive. Applying a renormalization
group method, the corresponding shortest-path dimensions and random-walk
dimensions are calculated exactly for k=1 networks and for k=2 regular
networks; in other cases, they are estimated by numerical methods. Although,
s=1 holds for all representatives of this class, the above quantities are found
to depend on the details of the structure of networks controlled by k and other
parameters.Comment: 10 pages, 9 figure
The mixing time of the switch Markov chains: a unified approach
Since 1997 a considerable effort has been spent to study the mixing time of
switch Markov chains on the realizations of graphic degree sequences of simple
graphs. Several results were proved on rapidly mixing Markov chains on
unconstrained, bipartite, and directed sequences, using different mechanisms.
The aim of this paper is to unify these approaches. We will illustrate the
strength of the unified method by showing that on any -stable family of
unconstrained/bipartite/directed degree sequences the switch Markov chain is
rapidly mixing. This is a common generalization of every known result that
shows the rapid mixing nature of the switch Markov chain on a region of degree
sequences. Two applications of this general result will be presented. One is an
almost uniform sampler for power-law degree sequences with exponent
. The other one shows that the switch Markov chain on the
degree sequence of an Erd\H{o}s-R\'enyi random graph is asymptotically
almost surely rapidly mixing if is bounded away from 0 and 1 by at least
.Comment: Clarification
Distances in random graphs with finite variance degrees
In this paper we study a random graph with nodes, where node has
degree and are i.i.d. with \prob(D_j\leq x)=F(x). We
assume that for some and some constant
. This graph model is a variant of the so-called configuration model, and
includes heavy tail degrees with finite variance.
The minimal number of edges between two arbitrary connected nodes, also known
as the graph distance or the hopcount, is investigated when . We
prove that the graph distance grows like , when the base of the
logarithm equals \nu=\expec[D_j(D_j -1)]/\expec[D_j]>1. This confirms the
heuristic argument of Newman, Strogatz and Watts \cite{NSW00}. In addition, the
random fluctuations around this asymptotic mean are
characterized and shown to be uniformly bounded. In particular, we show
convergence in distribution of the centered graph distance along exponentially
growing subsequences.Comment: 40 pages, 2 figure
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