14 research outputs found
Phase Transition in the Aldous-Shields Model of Growing Trees
We study analytically the late time statistics of the number of particles in
a growing tree model introduced by Aldous and Shields. In this model, a cluster
grows in continuous time on a binary Cayley tree, starting from the root, by
absorbing new particles at the empty perimeter sites at a rate proportional to
c^{-l} where c is a positive parameter and l is the distance of the perimeter
site from the root. For c=1, this model corresponds to random binary search
trees and for c=2 it corresponds to digital search trees in computer science.
By introducing a backward Fokker-Planck approach, we calculate the mean and the
variance of the number of particles at large times and show that the variance
undergoes a `phase transition' at a critical value c=sqrt{2}. While for
c>sqrt{2} the variance is proportional to the mean and the distribution is
normal, for c<sqrt{2} the variance is anomalously large and the distribution is
non-Gaussian due to the appearance of extreme fluctuations. The model is
generalized to one where growth occurs on a tree with branches and, in this
more general case, we show that the critical point occurs at c=sqrt{m}.Comment: Latex 17 pages, 6 figure
Exact eigenvalue spectrum of a class of fractal scale-free networks
The eigenvalue spectrum of the transition matrix of a network encodes
important information about its structural and dynamical properties. We study
the transition matrix of a family of fractal scale-free networks and
analytically determine all the eigenvalues and their degeneracies. We then use
these eigenvalues to evaluate the closed-form solution to the eigentime for
random walks on the networks under consideration. Through the connection
between the spectrum of transition matrix and the number of spanning trees, we
corroborate the obtained eigenvalues and their multiplicities.Comment: Definitive version accepted for publication in EPL (Europhysics
Letters
First Passage Properties of the Erdos-Renyi Random Graph
We study the mean time for a random walk to traverse between two arbitrary
sites of the Erdos-Renyi random graph. We develop an effective medium
approximation that predicts that the mean first-passage time between pairs of
nodes, as well as all moments of this first-passage time, are insensitive to
the fraction p of occupied links. This prediction qualitatively agrees with
numerical simulations away from the percolation threshold. Near the percolation
threshold, the statistically meaningful quantity is the mean transit rate,
namely, the inverse of the first-passage time. This rate varies
non-monotonically with p near the percolation transition. Much of this behavior
can be understood by simple heuristic arguments.Comment: 10 pages, 9 figures, 2-column revtex4 forma
Almost uniform sampling via quantum walks
Many classical randomized algorithms (e.g., approximation algorithms for
#P-complete problems) utilize the following random walk algorithm for {\em
almost uniform sampling} from a state space of cardinality : run a
symmetric ergodic Markov chain on for long enough to obtain a random
state from within total variation distance of the uniform
distribution over . The running time of this algorithm, the so-called {\em
mixing time} of , is , where
is the spectral gap of .
We present a natural quantum version of this algorithm based on repeated
measurements of the {\em quantum walk} . We show that it
samples almost uniformly from with logarithmic dependence on
just as the classical walk does; previously, no such
quantum walk algorithm was known. We then outline a framework for analyzing its
running time and formulate two plausible conjectures which together would imply
that it runs in time when is
the standard transition matrix of a constant-degree graph. We prove each
conjecture for a subclass of Cayley graphs.Comment: 13 pages; v2 added NSF grant info; v3 incorporated feedbac
Exit and Occupation times for Brownian Motion on Graphs with General Drift and Diffusion Constant
We consider a particle diffusing along the links of a general graph
possessing some absorbing vertices. The particle, with a spatially-dependent
diffusion constant D(x) is subjected to a drift U(x) that is defined in every
point of each link. We establish the boundary conditions to be used at the
vertices and we derive general expressions for the average time spent on a part
of the graph before absorption and, also, for the Laplace transform of the
joint law of the occupation times. Exit times distributions and splitting
probabilities are also studied and several examples are discussed.Comment: Accepted for publication in J. Phys.
Networking - A Statistical Physics Perspective
Efficient networking has a substantial economic and societal impact in a
broad range of areas including transportation systems, wired and wireless
communications and a range of Internet applications. As transportation and
communication networks become increasingly more complex, the ever increasing
demand for congestion control, higher traffic capacity, quality of service,
robustness and reduced energy consumption require new tools and methods to meet
these conflicting requirements. The new methodology should serve for gaining
better understanding of the properties of networking systems at the macroscopic
level, as well as for the development of new principled optimization and
management algorithms at the microscopic level. Methods of statistical physics
seem best placed to provide new approaches as they have been developed
specifically to deal with non-linear large scale systems. This paper aims at
presenting an overview of tools and methods that have been developed within the
statistical physics community and that can be readily applied to address the
emerging problems in networking. These include diffusion processes, methods
from disordered systems and polymer physics, probabilistic inference, which
have direct relevance to network routing, file and frequency distribution, the
exploration of network structures and vulnerability, and various other
practical networking applications.Comment: (Review article) 71 pages, 14 figure
Mathematical foundations of the Markov chain Monte Carlo method
7.2 was jointly undertaken with Vivek Gore, and is published here for the first time. I also thank an anonymous referee for carefully reading and providing helpful comments on a draft of this chapter. 1. Introduction The classical Monte Carlo method is an approach to estimating quantities that are hard to compute exactly. The quantity z of interest is expressed as the expectation z = ExpZ of a random variable (r.v.) Z for which some efficient sampling procedure is available. By taking the mean of some sufficiently large set of independent samples of Z, one may obtain an approximation to z. For example, suppose S = \Phi (x; y) 2 [0; 1] 2 : p i (x; y) 0; for all i \Psi<F1