576 research outputs found

### The Social Network of Contemporary Popular Musicians

In this paper we analyze two social network datasets of contemporary
musicians constructed from allmusic.com (AMG), a music and artists' information
database: one is the collaboration network in which two musicians are connected
if they have performed in or produced an album together, and the other is the
similarity network in which they are connected if they where musically similar
according to music experts. We find that, while both networks exhibit typical
features of social networks such as high transitivity, several key network
features, such as degree as well as betweenness distributions suggest
fundamental differences in music collaborations and music similarity networks
are created.Comment: 7 pages, 2 figure

### 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

### 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, $e^{-\beta \tau}$, where $\tau$ 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 $\beta$ for small values of $\beta$
and decays exponentially for large values of $\beta$. 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.

### Parameter estimators of random intersection graphs with thinned communities

This paper studies a statistical network model generated by a large number of
randomly sized overlapping communities, where any pair of nodes sharing a
community is linked with probability $q$ via the community. In the special case
with $q=1$ the model reduces to a random intersection graph which is known to
generate high levels of transitivity also in the sparse context. The parameter
$q$ adds a degree of freedom and leads to a parsimonious and analytically
tractable network model with tunable density, transitivity, and degree
fluctuations. We prove that the parameters of this model can be consistently
estimated in the large and sparse limiting regime using moment estimators based
on partially observed densities of links, 2-stars, and triangles.Comment: 15 page

### Distance, dissimilarity index, and network community structure

We address the question of finding the community structure of a complex
network. In an earlier effort [H. Zhou, {\em Phys. Rev. E} (2003)], the concept
of network random walking is introduced and a distance measure defined. Here we
calculate, based on this distance measure, the dissimilarity index between
nearest-neighboring vertices of a network and design an algorithm to partition
these vertices into communities that are hierarchically organized. Each
community is characterized by an upper and a lower dissimilarity threshold. The
algorithm is applied to several artificial and real-world networks, and
excellent results are obtained. In the case of artificially generated random
modular networks, this method outperforms the algorithm based on the concept of
edge betweenness centrality. For yeast's protein-protein interaction network,
we are able to identify many clusters that have well defined biological
functions.Comment: 10 pages, 7 figures, REVTeX4 forma

### Constrained spin dynamics description of random walks on hierarchical scale-free networks

We study a random walk problem on the hierarchical network which is a
scale-free network grown deterministically. The random walk problem is mapped
onto a dynamical Ising spin chain system in one dimension with a nonlocal spin
update rule, which allows an analytic approach. We show analytically that the
characteristic relaxation time scale grows algebraically with the total number
of nodes $N$ as $T \sim N^z$. From a scaling argument, we also show the
power-law decay of the autocorrelation function C_{\bfsigma}(t)\sim
t^{-\alpha}, which is the probability to find the Ising spins in the initial
state {\bfsigma} after $t$ time steps, with the state-dependent non-universal
exponent $\alpha$. It turns out that the power-law scaling behavior has its
origin in an quasi-ultrametric structure of the configuration space.Comment: 9 pages, 6 figure

### 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

### Scale free networks from a Hamiltonian dynamics

Contrary to many recent models of growing networks, we present a model with
fixed number of nodes and links, where it is introduced a dynamics favoring the
formation of links between nodes with degree of connectivity as different as
possible. By applying a local rewiring move, the network reaches equilibrium
states assuming broad degree distributions, which have a power law form in an
intermediate range of the parameters used. Interestingly, in the same range we
find non-trivial hierarchical clustering.Comment: 4 pages, revtex4, 5 figures. v2: corrected statements about
equilibriu

### A simple physical model for scaling in protein-protein interaction networks

It has recently been demonstrated that many biological networks exhibit a
scale-free topology where the probability of observing a node with a certain
number of edges (k) follows a power law: i.e. p(k) ~ k^-g. This observation has
been reproduced by evolutionary models. Here we consider the network of
protein-protein interactions and demonstrate that two published independent
measurements of these interactions produce graphs that are only weakly
correlated with one another despite their strikingly similar topology. We then
propose a physical model based on the fundamental principle that (de)solvation
is a major physical factor in protein-protein interactions. This model
reproduces not only the scale-free nature of such graphs but also a number of
higher-order correlations in these networks. A key support of the model is
provided by the discovery of a significant correlation between number of
interactions made by a protein and the fraction of hydrophobic residues on its
surface. The model presented in this paper represents the first physical model
for experimentally determined protein-protein interactions that comprehensively
reproduces the topological features of interaction networks. These results have
profound implications for understanding not only protein-protein interactions
but also other types of scale-free networks.Comment: 50 pages, 17 figure

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