461 research outputs found
The Network of Scientific Collaborations within the European Framework Programme
We use the emergent field of Complex Networks to analyze the network of
scientific collaborations between entities (universities, research
organizations, industry related companies,...) which collaborate in the context
of the so-called Framework Programme. We demonstrate here that it is a
scale--free network with an accelerated growth, which implies that the creation
of new collaborations is encouraged. Moreover, these collaborations possess
hierarchical modularity. Likewise, we find that the information flow depends on
the size of the participants but not on geographical constraints.Comment: 13 pages, 6 figure
Rank-based model for weighted network with hierarchical organization and disassortative mixing
Motivated by a recently introduced network growth mechanism that rely on the
ranking of node prestige measures [S. Fortunato \emph{et al}., Phys. Rev. Lett.
\textbf{96}, 218701 (2006)], a rank-based model for weighted network evolution
is studied. The evolution rule of the network is based on the ranking of node
strength, which couples the topological growth and the weight dynamics. Both
analytical solutions and numerical simulations show that the generated networks
possess scale-free distributions of degree, strength, and weight in the whole
region of the growth dynamics parameter (). We also characterize the
clustering and correlation properties of this class of networks. It is showed
that at a structural phase transition occurs, and for the
generated network simultaneously exhibits hierarchical organization and
disassortative degree correlation, which is consistent with a wide range of
biological networks.Comment: 4 pages, 5 figure
Hierarchical organization of modularity in metabolic networks
Spatially or chemically isolated functional modules composed of several
cellular components and carrying discrete functions are considered fundamental
building blocks of cellular organization, but their presence in highly
integrated biochemical networks lacks quantitative support. Here we show that
the metabolic networks of 43 distinct organisms are organized into many small,
highly connected topologic modules that combine in a hierarchical manner into
larger, less cohesive units, their number and degree of clustering following a
power law. Within Escherichia coli the uncovered hierarchical modularity
closely overlaps with known metabolic functions. The identified network
architecture may be generic to system-level cellular organization
Correlation between clustering and degree in affiliation networks
We are interested in the probability that two randomly selected neighbors of
a random vertex of degree (at least) are adjacent. We evaluate this
probability for a power law random intersection graph, where each vertex is
prescribed a collection of attributes and two vertices are adjacent whenever
they share a common attribute. We show that the probability obeys the scaling
as . Our results are mathematically rigorous. The
parameter is determined by the tail indices of power law
random weights defining the links between vertices and attributes
Hierarchical characterization of complex networks
While the majority of approaches to the characterization of complex networks
has relied on measurements considering only the immediate neighborhood of each
network node, valuable information about the network topological properties can
be obtained by considering further neighborhoods. The current work discusses on
how the concepts of hierarchical node degree and hierarchical clustering
coefficient (introduced in cond-mat/0408076), complemented by new hierarchical
measurements, can be used in order to obtain a powerful set of topological
features of complex networks. The interpretation of such measurements is
discussed, including an analytical study of the hierarchical node degree for
random networks, and the potential of the suggested measurements for the
characterization of complex networks is illustrated with respect to simulations
of random, scale-free and regular network models as well as real data
(airports, proteins and word associations). The enhanced characterization of
the connectivity provided by the set of hierarchical measurements also allows
the use of agglomerative clustering methods in order to obtain taxonomies of
relationships between nodes in a network, a possibility which is also
illustrated in the current article.Comment: 19 pages, 23 figure
The effect of aging on network structure
In network evolution, the effect of aging is universal: in scientific
collaboration network, scientists have a finite time span of being active; in
movie actors network, once popular stars are retiring from stage; devices on
the Internet may become outmoded with techniques developing so rapidly. Here we
find in citation networks that this effect can be represented by an exponential
decay factor, , where is the node age, while other
evolving networks (the Internet for instance) may have different types of
aging, for example, a power-law decay factor, which is also studied and
compared. It has been found that as soon as such a factor is introduced to the
Barabasi-Albert Scale-Free model, the network will be significantly
transformed. The network will be clustered even with infinitely large size, and
the clustering coefficient varies greatly with the intensity of the aging
effect, i.e. it increases linearly with for small values of
and decays exponentially for large values of . At the same time, the
aging effect may also result in a hierarchical structure and a disassortative
degree-degree correlation. Generally the aging effect will increase the average
distance between nodes, but the result depends on the type of the decay factor.
The network appears like a one-dimensional chain when exponential decay is
chosen, but with power-law decay, a transformation process is observed, i.e.,
from a small-world network to a hypercubic lattice, and to a one-dimensional
chain finally. The disparities observed for different choices of the decay
factor, in clustering, average node distance and probably other aspects not yet
identified, are believed to bear significant meaning on empirical data
acquisition.Comment: 8 pages, 9 figures,V2, accepted for publication in Phys. Rev.
Vertex similarity in networks
We consider methods for quantifying the similarity of vertices in networks.
We propose a measure of similarity based on the concept that two vertices are
similar if their immediate neighbors in the network are themselves similar.
This leads to a self-consistent matrix formulation of similarity that can be
evaluated iteratively using only a knowledge of the adjacency matrix of the
network. We test our similarity measure on computer-generated networks for
which the expected results are known, and on a number of real-world networks
Effect of correlations on network controllability
A dynamical system is controllable if by imposing appropriate external
signals on a subset of its nodes, it can be driven from any initial state to
any desired state in finite time. Here we study the impact of various network
characteristics on the minimal number of driver nodes required to control a
network. We find that clustering and modularity have no discernible impact, but
the symmetries of the underlying matching problem can produce linear, quadratic
or no dependence on degree correlation coefficients, depending on the nature of
the underlying correlations. The results are supported by numerical simulations
and help narrow the observed gap between the predicted and the observed number
of driver nodes in real networks
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