85 research outputs found
Quasistatic Scale-free Networks
A network is formed using the sites of an one-dimensional lattice in the
shape of a ring as nodes and each node with the initial degree .
links are then introduced to this network, each link starts from a distinct
node, the other end being connected to any other node with degree randomly
selected with an attachment probability proportional to . Tuning
the control parameter we observe a transition where the average degree
of the largest node changes its variation from to
at a specific transition point of . The network is scale-free i.e.,
the nodal degree distribution has a power law decay for .Comment: 4 pages, 5 figure
A Geometric Fractal Growth Model for Scale Free Networks
We introduce a deterministic model for scale-free networks, whose degree
distribution follows a power-law with the exponent . At each time step,
each vertex generates its offsprings, whose number is proportional to the
degree of that vertex with proportionality constant m-1 (m>1). We consider the
two cases: first, each offspring is connected to its parent vertex only,
forming a tree structure, and secondly, it is connected to both its parent and
grandparent vertices, forming a loop structure. We find that both models
exhibit power-law behaviors in their degree distributions with the exponent
. Thus, by tuning m, the degree exponent can be
adjusted in the range, . We also solve analytically a mean
shortest-path distance d between two vertices for the tree structure, showing
the small-world behavior, that is, , where N is
system size, and is the mean degree. Finally, we consider the case
that the number of offsprings is the same for all vertices, and find that the
degree distribution exhibits an exponential-decay behavior
Stability of shortest paths in complex networks with random edge weights
We study shortest paths and spanning trees of complex networks with random
edge weights. Edges which do not belong to the spanning tree are inactive in a
transport process within the network. The introduction of quenched disorder
modifies the spanning tree such that some edges are activated and the network
diameter is increased. With analytic random-walk mappings and numerical
analysis, we find that the spanning tree is unstable to the introduction of
disorder and displays a phase-transition-like behavior at zero disorder
strength . In the infinite network-size limit (), we
obtain a continuous transition with the density of activated edges
growing like and with the diameter-expansion coefficient
growing like in the regular network, and
first-order transitions with discontinuous jumps in and at
for the small-world (SW) network and the Barab\'asi-Albert
scale-free (SF) network. The asymptotic scaling behavior sets in when , where the crossover size scales as for the
regular network, for the SW network, and
for the SF network. In a
transient regime with , there is an infinite-order transition with
for the SW network
and for the SF network. It
shows that the transport pattern is practically most stable in the SF network.Comment: 9 pages, 7 figur
Structured Model-Based Analysis and Control of the Hyaluronic Acid Fermentation by Streptococcus zooepidemicus: Physiological Implications of Glucose and Complex-Nitrogen-Limited Growth
Pseudofractal Scale-free Web
We find that scale-free random networks are excellently modeled by a
deterministic graph. This graph has a discrete degree distribution (degree is
the number of connections of a vertex) which is characterized by a power-law
with exponent . Properties of this simple structure are
surprisingly close to those of growing random scale-free networks with
in the most interesting region, between 2 and 3. We succeed to find exactly and
numerically with high precision all main characteristics of the graph. In
particular, we obtain the exact shortest-path-length distribution. For the
large network () the distribution tends to a Gaussian of width
centered at . We show that the
eigenvalue spectrum of the adjacency matrix of the graph has a power-law tail
with exponent .Comment: 5 pages, 3 figure
Reduced Hypoxia Risk in a Systemic Sclerosis Patient with Interstitial Lung Disease after Long-Term Pulmonary Rehabilitation
Pulmonary rehabilitation is effective for improving exercise capacity in patients with interstitial lung disease (ILD), and most programs last about 8 weeks. A 43-year-old male patient with systemic sclerosis and oxygen saturation (SpO2) declining because of severe ILD was hospitalized for treatment of chronic skin ulcers. During admission, he completed a 27-week walking exercise program with SpO2 monitoring. Consequently, continuous walking distance without severe hypoxia (SpO2 > 90%) increased from 60 m to 300 m after the program, although his six-minute walking distance remained the same. This suggests that walking exercise for several months may reduce the risk of hypoxia in patients with ILD, even though exercise capacity does not improve
Correlations in Scale-Free Networks: Tomography and Percolation
We discuss three related models of scale-free networks with the same degree
distribution but different correlation properties. Starting from the
Barabasi-Albert construction based on growth and preferential attachment we
discuss two other networks emerging when randomizing it with respect to links
or nodes. We point out that the Barabasi-Albert model displays dissortative
behavior with respect to the nodes' degrees, while the node-randomized network
shows assortative mixing. These kinds of correlations are visualized by
discussig the shell structure of the networks around their arbitrary node. In
spite of different correlation behavior, all three constructions exhibit
similar percolation properties.Comment: 6 pages, 2 figures; added reference
First report of banana bunchy top virus in banana (Musa spp.) and its eradication in Togo
Open Access Article; Published online: 27 Apr 202
Class of correlated random networks with hidden variables
We study a class models of correlated random networks in which vertices are
characterized by \textit{hidden variables} controlling the establishment of
edges between pairs of vertices. We find analytical expressions for the main
topological properties of these models as a function of the distribution of
hidden variables and the probability of connecting vertices. The expressions
obtained are checked by means of numerical simulations in a particular example.
The general model is extended to describe a practical algorithm to generate
random networks with an \textit{a priori} specified correlation structure. We
also present an extension of the class, to map non-equilibrium growing networks
to networks with hidden variables that represent the time at which each vertex
was introduced in the system
Ophthalmic Complications of Dengue
A case series suggests that the spectrum of complications in dengue infection is widening
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