431 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.
Fluctuations in network dynamics
Most complex networks serve as conduits for various dynamical processes,
ranging from mass transfer by chemical reactions in the cell to packet transfer
on the Internet. We collected data on the time dependent activity of five
natural and technological networks, finding that for each the coupling of the
flux fluctuations with the total flux on individual nodes obeys a unique
scaling law. We show that the observed scaling can explain the competition
between the system's internal collective dynamics and changes in the external
environment, allowing us to predict the relevant scaling exponents.Comment: 4 pages, 4 figures. Published versio
Dynamic Effects Increasing Network Vulnerability to Cascading Failures
We study cascading failures in networks using a dynamical flow model based on
simple conservation and distribution laws to investigate the impact of
transient dynamics caused by the rebalancing of loads after an initial network
failure (triggering event). It is found that considering the flow dynamics may
imply reduced network robustness compared to previous static overload failure
models. This is due to the transient oscillations or overshooting in the loads,
when the flow dynamics adjusts to the new (remaining) network structure. We
obtain {\em upper} and {\em lower} limits to network robustness, and it is
shown that {\it two} time scales and , defined by the network
dynamics, are important to consider prior to accurately addressing network
robustness or vulnerability. The robustness of networks showing cascading
failures is generally determined by a complex interplay between the network
topology and flow dynamics, where the ratio determines the
relative role of the two of them.Comment: 4 pages Latex, 4 figure
Synchronization in Random Geometric Graphs
In this paper we study the synchronization properties of random geometric
graphs. We show that the onset of synchronization takes place roughly at the
same value of the order parameter that a random graph with the same size and
average connectivity. However, the dependence of the order parameter with the
coupling strength indicates that the fully synchronized state is more easily
attained in random graphs. We next focus on the complete synchronized state and
show that this state is less stable for random geometric graphs than for other
kinds of complex networks. Finally, a rewiring mechanism is proposed as a way
to improve the stability of the fully synchronized state as well as to lower
the value of the coupling strength at which it is achieved. Our work has
important implications for the synchronization of wireless networks, and should
provide valuable insights for the development and deployment of more efficient
and robust distributed synchronization protocols for these systems.Comment: 5 pages, 4 figure
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
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
Fingerprint for Network Topologies
A network's topology information can be given as an adjacency matrix. The
bitmap of sorted adjacency matrix(BOSAM) is a network visualisation tool which
can emphasise different network structures by just looking at reordered
adjacent matrixes. A BOSAM picture resembles the shape of a flower and is
characterised by a series of 'leaves'. Here we show and mathematically prove
that for most networks, there is a self-similar relation between the envelope
of the BOSAM leaves. This self-similar property allows us to use a single
envelope to predict all other envelopes and therefore reconstruct the outline
of a network's BOSAM picture. We analogise the BOSAM envelope to human's
fingerprint as they share a number of common features, e.g. both are simple,
easy to obtain, and strongly characteristic encoding essential information for
identification.Comment: 12papes, 3 figures, in pres
Transport on weighted Networks: when correlations are independent of degree
Most real-world networks are weighted graphs with the weight of the edges
reflecting the relative importance of the connections. In this work, we study
non degree dependent correlations between edge weights, generalizing thus the
correlations beyond the degree dependent case. We propose a simple method to
introduce weight-weight correlations in topologically uncorrelated graphs. This
allows us to test different measures to discriminate between the different
correlation types and to quantify their intensity. We also discuss here the
effect of weight correlations on the transport properties of the networks,
showing that positive correlations dramatically improve transport. Finally, we
give two examples of real-world networks (social and transport graphs) in which
weight-weight correlations are present.Comment: 8 pages, 8 figure
Universal scaling of distances in complex networks
Universal scaling of distances between vertices of Erdos-Renyi random graphs,
scale-free Barabasi-Albert models, science collaboration networks, biological
networks, Internet Autonomous Systems and public transport networks are
observed. A mean distance between two nodes of degrees k_i and k_j equals to
=A-B log(k_i k_j). The scaling is valid over several decades. A simple
theory for the appearance of this scaling is presented. Parameters A and B
depend on the mean value of a node degree _nn calculated for the nearest
neighbors and on network clustering coefficients.Comment: 4 pages, 3 figures, 1 tabl
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
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