398,682 research outputs found
Variance Analysis of Randomized Consensus in Switching Directed Networks
In this paper, we study the asymptotic properties of distributed consensus
algorithms over switching directed random networks. More specifically, we focus
on consensus algorithms over independent and identically distributed, directed
Erdos-Renyi random graphs, where each agent can communicate with any other
agent with some exogenously specified probability . While it is well-known
that consensus algorithms over Erdos-Renyi random networks result in an
asymptotic agreement over the network, an analytical characterization of the
distribution of the asymptotic consensus value is still an open question. In
this paper, we provide closed-form expressions for the mean and variance of the
asymptotic random consensus value, in terms of the size of the network and the
probability of communication . We also provide numerical simulations that
illustrate our results.Comment: 6 pages, 3 figures, submitted to American Control Conference 201
Distribution of shortest cycle lengths in random networks
We present analytical results for the distribution of shortest cycle lengths
(DSCL) in random networks. The approach is based on the relation between the
DSCL and the distribution of shortest path lengths (DSPL). We apply this
approach to configuration model networks, for which analytical results for the
DSPL were obtained before. We first calculate the fraction of nodes in the
network which reside on at least one cycle. Conditioning on being on a cycle,
we provide the DSCL over ensembles of configuration model networks with degree
distributions which follow a Poisson distribution (Erdos-R\'enyi network),
degenerate distribution (random regular graph) and a power-law distribution
(scale-free network). The mean and variance of the DSCL are calculated. The
analytical results are found to be in very good agreement with the results of
computer simulations.Comment: 44 pages, 11 figure
Scaling behavior of the contact process in networks with long-range connections
We present simulation results for the contact process on regular, cubic
networks that are composed of a one-dimensional lattice and a set of long edges
with unbounded length. Networks with different sets of long edges are
considered, that are characterized by different shortest-path dimensions and
random-walk dimensions. We provide numerical evidence that an absorbing phase
transition occurs at some finite value of the infection rate and the
corresponding dynamical critical exponents depend on the underlying network.
Furthermore, the time-dependent quantities exhibit log-periodic oscillations in
agreement with the discrete scale invariance of the networks. In case of
spreading from an initial active seed, the critical exponents are found to
depend on the location of the initial seed and break the hyper-scaling law of
the directed percolation universality class due to the inhomogeneity of the
networks. However, if the cluster spreading quantities are averaged over
initial sites the hyper-scaling law is restored.Comment: 9 pages, 10 figure
Entanglement percolation in quantum complex networks
Quantum networks are essential to quantum information distributed
applications, and communicating over them is a key challenge. Complex networks
have rich and intriguing properties, which are as yet unexplored in the quantum
setting. Here, we study the effect of entanglement percolation as a means to
establish long-distance entanglement between arbitrary nodes of quantum complex
networks. We develop a theory to analytically study random graphs with
arbitrary degree distribution and give exact results for some models. Our
findings are in good agreement with numerical simulations and show that the
proposed quantum strategies enhance the percolation threshold substantially.
Simulations also show a clear enhancement in small-world and other real-world
networks
A network epidemic model with preventive rewiring: comparative analysis of the initial phase
This paper is concerned with stochastic SIR and SEIR epidemic models on
random networks in which individuals may rewire away from infected neighbors at
some rate (and reconnect to non-infectious individuals with
probability or else simply drop the edge if ), so-called
preventive rewiring. The models are denoted SIR- and SEIR-, and
we focus attention on the early stages of an outbreak, where we derive
expression for the basic reproduction number and the expected degree of
the infectious nodes using two different approximation approaches. The
first approach approximates the early spread of an epidemic by a branching
process, whereas the second one uses pair approximation. The expressions are
compared with the corresponding empirical means obtained from stochastic
simulations of SIR- and SEIR- epidemics on Poisson and
scale-free networks. Without rewiring of exposed nodes, the two approaches
predict the same epidemic threshold and the same for both types of
epidemics, the latter being very close to the mean degree obtained from
simulated epidemics over Poisson networks. Above the epidemic threshold,
pairwise models overestimate the value of computed from simulations,
which turns out to be very close to the one predicted by the branching process
approximation. When exposed individuals also rewire with (perhaps
unaware of being infected), the two approaches give different epidemic
thresholds, with the branching process approximation being more in agreement
with simulations.Comment: 25 pages, 7 figure
Random features and polynomial rules
Random features models play a distinguished role in the theory of deep
learning, describing the behavior of neural networks close to their
infinite-width limit. In this work, we present a thorough analysis of the
generalization performance of random features models for generic supervised
learning problems with Gaussian data. Our approach, built with tools from the
statistical mechanics of disordered systems, maps the random features model to
an equivalent polynomial model, and allows us to plot average generalization
curves as functions of the two main control parameters of the problem: the
number of random features and the size of the training set, both
assumed to scale as powers in the input dimension . Our results extend the
case of proportional scaling between , and . They are in accordance
with rigorous bounds known for certain particular learning tasks and are in
quantitative agreement with numerical experiments performed over many order of
magnitudes of and . We find good agreement also far from the asymptotic
limits where and at least one between , remains
finite.Comment: 11 pages + appendix, 4 figures. Comments are welcom
Generalized two-body self-consistent theory of random linear dielectric composites: an effective-medium approach to clustering in highly-disordered media
Effects of two-body dipolar interactions on the effective
permittivity/conductivity of a binary, symmetric, random dielectric composite
are investigated in a self-consistent framework. By arbitrarily splitting the
singularity of the Green tensor of the electric field, we introduce an
additional degree of freedom into the problem, in the form of an unknown
"inner" depolarization constant. Two coupled self-consistent equations
determine the latter and the permittivity in terms of the dielectric contrast
and the volume fractions. One of them generalizes the usual Coherent Potential
condition to many-body interactions between single-phase clusters of
polarizable matter elements, while the other one determines the effective
medium in which clusters are embedded. The latter is in general different from
the overall permittivity. The proposed approach allows for many-body
corrections to the Bruggeman-Landauer (BL) scheme to be handled in a
multiple-scattering framework. Four parameters are used to adjust the degree of
self-consistency and to characterize clusters in a schematic geometrical way.
Given these parameters, the resulting theory is "exact" to second order in the
volume fractions. For suitable parameter values, reasonable to excellent
agreement is found between theory and simulations of random-resistor networks
and pixelwise-disordered arrays in two and tree dimensions, over the whole
range of volume fractions. Comparisons with simulation data are made using an
"effective" scalar depolarization constant that constitutes a very sensitive
indicator of deviations from the BL theory.Comment: 14 pages, 7 figure
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