188 research outputs found
Translations and dynamics
We analyze the role played by local translational symmetry in the context of
gauge theories of fundamental interactions. Translational connections and
fields are introduced, with special attention being paid to their universal
coupling to other variables, as well as to their contributions to field
equations and to conserved quantities.Comment: 22 Revtex pages, no figures. Published version with minor correction
Thresholds for epidemic spreading in networks
We study the threshold of epidemic models in quenched networks with degree
distribution given by a power-law. For the susceptible-infected-susceptible
(SIS) model the activity threshold lambda_c vanishes in the large size limit on
any network whose maximum degree k_max diverges with the system size, at odds
with heterogeneous mean-field (HMF) theory. The vanishing of the threshold has
not to do with the scale-free nature of the connectivity pattern and is instead
originated by the largest hub in the system being active for any spreading rate
lambda>1/sqrt{k_max} and playing the role of a self-sustained source that
spreads the infection to the rest of the system. The
susceptible-infected-removed (SIR) model displays instead agreement with HMF
theory and a finite threshold for scale-rich networks. We conjecture that on
quenched scale-rich networks the threshold of generic epidemic models is
vanishing or finite depending on the presence or absence of a steady state.Comment: 5 pages, 4 figure
Mean-field diffusive dynamics on weighted networks
Diffusion is a key element of a large set of phenomena occurring on natural
and social systems modeled in terms of complex weighted networks. Here, we
introduce a general formalism that allows to easily write down mean-field
equations for any diffusive dynamics on weighted networks. We also propose the
concept of annealed weighted networks, in which such equations become exact. We
show the validity of our approach addressing the problem of the random walk
process, pointing out a strong departure of the behavior observed in quenched
real scale-free networks from the mean-field predictions. Additionally, we show
how to employ our formalism for more complex dynamics. Our work sheds light on
mean-field theory on weighted networks and on its range of validity, and warns
about the reliability of mean-field results for complex dynamics.Comment: 8 pages, 3 figure
A cosmological model in Weyl-Cartan spacetime
We present a cosmological model for early stages of the universe on the basis
of a Weyl-Cartan spacetime. In this model, torsion and
nonmetricity are proportional to the vacuum polarization.
Extending earlier work of one of us (RT), we discuss the behavior of the cosmic
scale factor and the Weyl 1-form in detail. We show how our model fits into the
more general framework of metric-affine gravity (MAG).Comment: 19 pages, 5 figures, typos corrected, uses IOP style fil
The Husain-Kuchar Model: Time Variables and Non-degenerate Metrics
We study the Husain-Kuchar model by introducing a new action principle
similar to the self-dual action used in the Ashtekar variables approach to
Quantum Gravity. This new action has several interesting features; among them,
the presence of a scalar time variable that allows the definition of geometric
observables without adding new degrees of freedom, the appearance of a natural
non-degenerate four-metric and the possibility of coupling ordinary matter.Comment: LaTeX, 22 pages, accepted for publication in Phys. Rev.
Generation of uncorrelated random scale-free networks
Uncorrelated random scale-free networks are useful null models to check the
accuracy an the analytical solutions of dynamical processes defined on complex
networks. We propose and analyze a model capable to generate random
uncorrelated scale-free networks with no multiple and self-connections. The
model is based on the classical configuration model, with an additional
restriction on the maximum possible degree of the vertices. We check
numerically that the proposed model indeed generates scale-free networks with
no two and three vertex correlations, as measured by the average degree of the
nearest neighbors and the clustering coefficient of the vertices of degree ,
respectively
Diffusion-annihilation processes in complex networks
We present a detailed analytical study of the
diffusion-annihilation process in complex networks. By means of microscopic
arguments, we derive a set of rate equations for the density of particles
in vertices of a given degree, valid for any generic degree distribution, and
which we solve for uncorrelated networks. For homogeneous networks (with
bounded fluctuations), we recover the standard mean-field solution, i.e. a
particle density decreasing as the inverse of time. For heterogeneous
(scale-free networks) in the infinite network size limit, we obtain instead a
density decreasing as a power-law, with an exponent depending on the degree
distribution. We also analyze the role of finite size effects, showing that any
finite scale-free network leads to the mean-field behavior, with a prefactor
depending on the network size. We check our analytical predictions with
extensive numerical simulations on homogeneous networks with Poisson degree
distribution and scale-free networks with different degree exponents.Comment: 9 pages, 5 EPS figure
Slow dynamics and rare-region effects in the contact process on weighted tree networks
We show that generic, slow dynamics can occur in the contact process on
complex networks with a tree-like structure and a superimposed weight pattern,
in the absence of additional (non-topological) sources of quenched disorder.
The slow dynamics is induced by rare-region effects occurring on correlated
subspaces of vertices connected by large weight edges, and manifests in the
form of a smeared phase transition. We conjecture that more sophisticated
network motifs could be able to induce Griffiths phases, as a consequence of
purely topological disorder.Comment: 12 pages, 10 figures, final version appeared in PR
Random walks and search in time-varying networks
The random walk process underlies the description of a large number of real
world phenomena. Here we provide the study of random walk processes in time
varying networks in the regime of time-scale mixing; i.e. when the network
connectivity pattern and the random walk process dynamics are unfolding on the
same time scale. We consider a model for time varying networks created from the
activity potential of the nodes, and derive solutions of the asymptotic
behavior of random walks and the mean first passage time in undirected and
directed networks. Our findings show striking differences with respect to the
well known results obtained in quenched and annealed networks, emphasizing the
effects of dynamical connectivity patterns in the definition of proper
strategies for search, retrieval and diffusion processes in time-varying
network
Diffusion-annihilation processes in complex networks
We present a detailed analytical study of the
diffusion-annihilation process in complex networks. By means of microscopic
arguments, we derive a set of rate equations for the density of particles
in vertices of a given degree, valid for any generic degree distribution, and
which we solve for uncorrelated networks. For homogeneous networks (with
bounded fluctuations), we recover the standard mean-field solution, i.e. a
particle density decreasing as the inverse of time. For heterogeneous
(scale-free networks) in the infinite network size limit, we obtain instead a
density decreasing as a power-law, with an exponent depending on the degree
distribution. We also analyze the role of finite size effects, showing that any
finite scale-free network leads to the mean-field behavior, with a prefactor
depending on the network size. We check our analytical predictions with
extensive numerical simulations on homogeneous networks with Poisson degree
distribution and scale-free networks with different degree exponents.Comment: 9 pages, 5 EPS figure
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