371 research outputs found
Designer Nets from Local Strategies
We propose a local strategy for constructing scale-free networks of arbitrary
degree distributions, based on the redirection method of Krapivsky and Redner
[Phys. Rev. E 63, 066123 (2001)]. Our method includes a set of external
parameters that can be tuned at will to match detailed behavior at small degree
k, in addition to the scale-free power-law tail signature at large k. The
choice of parameters determines other network characteristics, such as the
degree of clustering. The method is local in that addition of a new node
requires knowledge of only the immediate environs of the (randomly selected)
node to which it is attached. (Global strategies require information on finite
fractions of the growing net.
Annealing schedule from population dynamics
We introduce a dynamical annealing schedule for population-based optimization
algorithms with mutation. On the basis of a statistical mechanics formulation
of the population dynamics, the mutation rate adapts to a value maximizing
expected rewards at each time step. Thereby, the mutation rate is eliminated as
a free parameter from the algorithm.Comment: 6 pages RevTeX, 4 figures PostScript; to be published in Phys. Rev.
Effect of degree correlations above the first shell on the percolation transition
The use of degree-degree correlations to model realistic networks which are
characterized by their Pearson's coefficient, has become widespread. However
the effect on how different correlation algorithms produce different results on
processes on top of them, has not yet been discussed. In this letter, using
different correlation algorithms to generate assortative networks, we show that
for very assortative networks the behavior of the main observables in
percolation processes depends on the algorithm used to build the network. The
different alghoritms used here introduce different inner structures that are
missed in Pearson's coefficient. We explain the different behaviors through a
generalization of Pearson's coefficient that allows to study the correlations
at chemical distances l from a root node. We apply our findings to real
networks.Comment: In press EP
Thermodynamic forces, flows, and Onsager coefficients in complex networks
We present Onsager formalism applied to random networks with arbitrary degree
distribution. Using the well-known methods of non-equilibrium thermodynamics we
identify thermodynamic forces and their conjugated flows induced in networks as
a result of single node degree perturbation. The forces and the flows can be
understood as a response of the system to events, such as random removal of
nodes or intentional attacks on them. Finally, we show that cross effects (such
as thermodiffusion, or thermoelectric phenomena), in which one force may not
only give rise to its own corresponding flow, but to many other flows, can be
observed also in complex networks.Comment: 4 pages, 2 figure
Percolation in Hierarchical Scale-Free Nets
We study the percolation phase transition in hierarchical scale-free nets.
Depending on the method of construction, the nets can be fractal or small-world
(the diameter grows either algebraically or logarithmically with the net size),
assortative or disassortative (a measure of the tendency of like-degree nodes
to be connected to one another), or possess various degrees of clustering. The
percolation phase transition can be analyzed exactly in all these cases, due to
the self-similar structure of the hierarchical nets. We find different types of
criticality, illustrating the crucial effect of other structural properties
besides the scale-free degree distribution of the nets.Comment: 9 Pages, 11 figures. References added and minor corrections to
manuscript. In pres
Topological Evolution of Dynamical Networks: Global Criticality from Local Dynamics
We evolve network topology of an asymmetrically connected threshold network
by a simple local rewiring rule: quiet nodes grow links, active nodes lose
links. This leads to convergence of the average connectivity of the network
towards the critical value in the limit of large system size . How
this principle could generate self-organization in natural complex systems is
discussed for two examples: neural networks and regulatory networks in the
genome.Comment: 4 pages RevTeX, 4 figures PostScript, revised versio
Characterizing the network topology of the energy landscapes of atomic clusters
By dividing potential energy landscapes into basins of attractions
surrounding minima and linking those basins that are connected by transition
state valleys, a network description of energy landscapes naturally arises.
These networks are characterized in detail for a series of small Lennard-Jones
clusters and show behaviour characteristic of small-world and scale-free
networks. However, unlike many such networks, this topology cannot reflect the
rules governing the dynamics of network growth, because they are static spatial
networks. Instead, the heterogeneity in the networks stems from differences in
the potential energy of the minima, and hence the hyperareas of their
associated basins of attraction. The low-energy minima with large basins of
attraction act as hubs in the network.Comparisons to randomized networks with
the same degree distribution reveals structuring in the networks that reflects
their spatial embedding.Comment: 14 pages, 11 figure
Synchronization of Kuramoto Oscillators in Scale-Free Networks
In this work, we study the synchronization of coupled phase oscillators on
the underlying topology of scale-free networks. In particular, we assume that
each network's component is an oscillator and that each interacts with the
others following the Kuramoto model. We then study the onset of global phase
synchronization and fully characterize the system's dynamics. We also found
that the resynchronization time of a perturbed node decays as a power law of
its connectivity, providing a simple analytical explanation to this interesting
behavior.Comment: 7 pages and 4 eps figures, the text has been slightly modified and
new references have been included. Final version to appear in Europhysics
Letter
Statistical properties of absolute log-returns and a stochastic model of stock markets with heterogeneous agents
This paper is intended as an investigation of the statistical properties of
{\it absolute log-returns}, defined as the absolute value of the logarithmic
price change, for the Nikkei 225 index in the 28-year period from January 4,
1975 to December 30, 2002. We divided the time series of the Nikkei 225 index
into two periods, an inflationary period and a deflationary period. We have
previously [18] found that the distribution of absolute log-returns can be
approximated by the power-law distribution in the inflationary period, while
the distribution of absolute log-returns is well described by the exponential
distribution in the deflationary period.\par To further explore these empirical
findings, we have introduced a model of stock markets which was proposed in
[19,20]. In this model, the stock market is composed of two groups of traders:
{\it the fundamentalists}, who believe that the asset price will return to the
fundamental price, and {\it the interacting traders}, who can be noise traders.
We show through numerical simulation of the model that when the number of
interacting traders is greater than the number of fundamentalists, the
power-law distribution of absolute log-returns is generated by the interacting
traders' herd behavior, and, inversely, when the number of fundamentalists is
greater than the number of interacting traders, the exponential distribution of
absolute log-returns is generated.Comment: 12 pages, 5 figure
Fractal and Transfractal Recursive Scale-Free Nets
We explore the concepts of self-similarity, dimensionality, and
(multi)scaling in a new family of recursive scale-free nets that yield
themselves to exact analysis through renormalization techniques. All nets in
this family are self-similar and some are fractals - possessing a finite
fractal dimension - while others are small world (their diameter grows
logarithmically with their size) and are infinite-dimensional. We show how a
useful measure of "transfinite" dimension may be defined and applied to the
small world nets. Concerning multiscaling, we show how first-passage time for
diffusion and resistance between hub (the most connected nodes) scale
differently than for other nodes. Despite the different scalings, the Einstein
relation between diffusion and conductivity holds separately for hubs and
nodes. The transfinite exponents of small world nets obey Einstein relations
analogous to those in fractal nets.Comment: Includes small revisions and references added as result of readers'
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