6,712 research outputs found
Stability as a natural selection mechanism on interacting networks
Biological networks of interacting agents exhibit similar topological
properties for a wide range of scales, from cellular to ecological levels,
suggesting the existence of a common evolutionary origin. A general
evolutionary mechanism based on global stability has been proposed recently [J
I Perotti, O V Billoni, F A Tamarit, D R Chialvo, S A Cannas, Phys. Rev. Lett.
103, 108701 (2009)]. This mechanism is incorporated into a model of a growing
network of interacting agents in which each new agent's membership in the
network is determined by the agent's effect on the network's global stability.
We show that, out of this stability constraint, several topological properties
observed in biological networks emerge in a self organized manner. The
influence of the stability selection mechanism on the dynamics associated to
the resulting network is analyzed as well.Comment: 10 pages, 9 figure
Structural patterns in complex networks through spectral analysis
The study of some structural properties of networks is introduced from a graph spectral perspective. First, subgraph centrality of nodes is defined and used to classify essential proteins in a proteomic map. This index is then used to produce a method that allows the identification of superhomogeneous networks. At the same time this method classify non-homogeneous network into three universal classes of structure. We give examples of these classes from networks in different real-world scenarios. Finally, a communicability function is studied and showed as an alternative for defining communities in complex networks. Using this approach a community is unambiguously defined and an algorithm for its identification is proposed and exemplified in a real-world network
Locating influential nodes via dynamics-sensitive centrality
With great theoretical and practical significance, locating influential nodes
of complex networks is a promising issues. In this paper, we propose a
dynamics-sensitive (DS) centrality that integrates topological features and
dynamical properties. The DS centrality can be directly applied in locating
influential spreaders. According to the empirical results on four real networks
for both susceptible-infected-recovered (SIR) and susceptible-infected (SI)
spreading models, the DS centrality is much more accurate than degree,
-shell index and eigenvector centrality.Comment: 6 pages, 1 table and 2 figure
Detecting rich-club ordering in complex networks
Uncovering the hidden regularities and organizational principles of networks
arising in physical systems ranging from the molecular level to the scale of
large communication infrastructures is the key issue for the understanding of
their fabric and dynamical properties [1-5]. The ``rich-club'' phenomenon
refers to the tendency of nodes with high centrality, the dominant elements of
the system, to form tightly interconnected communities and it is one of the
crucial properties accounting for the formation of dominant communities in both
computer and social sciences [4-8]. Here we provide the analytical expression
and the correct null models which allow for a quantitative discussion of the
rich-club phenomenon. The presented analysis enables the measurement of the
rich-club ordering and its relation with the function and dynamics of networks
in examples drawn from the biological, social and technological domains.Comment: 1 table, 3 figure
Modular organisation of interaction networks based on asymptotic dynamics
This paper investigates questions related to the modularity in discrete
models of biological interaction networks. We develop a theoretical framework
based on the analysis of their asymptotic dynamics. More precisely, we exhibit
formal conditions under which agents of interaction networks can be grouped
into modules. As a main result, we show that the usual decomposition in
strongly connected components fulfils the conditions of being a modular
organisation. Furthermore, we point out that our framework enables a finer
analysis providing a decomposition in elementary modules
Michaelis-Menten Dynamics in Complex Heterogeneous Networks
Biological networks have been recently found to exhibit many topological
properties of the so-called complex networks. It has been reported that they
are, in general, both highly skewed and directed. In this paper, we report on
the dynamics of a Michaelis-Menten like model when the topological features of
the underlying network resemble those of real biological networks.
Specifically, instead of using a random graph topology, we deal with a complex
heterogeneous network characterized by a power-law degree distribution coupled
to a continuous dynamics for each network's component. The dynamics of the
model is very rich and stationary, periodic and chaotic states are observed
upon variation of the model's parameters. We characterize these states
numerically and report on several quantities such as the system's phase diagram
and size distributions of clusters of stationary, periodic and chaotic nodes.
The results are discussed in view of recent debate about the ubiquity of
complex networks in nature and on the basis of several biological processes
that can be well described by the dynamics studied.Comment: Paper enlarged and modified, including the title. Some problems with
the pdf were detected in the past. If they persist, please ask for the pdf by
e-mailing yamir(at_no_spam)unizar.es. Version to appear in Physica
Graph Theory and Networks in Biology
In this paper, we present a survey of the use of graph theoretical techniques
in Biology. In particular, we discuss recent work on identifying and modelling
the structure of bio-molecular networks, as well as the application of
centrality measures to interaction networks and research on the hierarchical
structure of such networks and network motifs. Work on the link between
structural network properties and dynamics is also described, with emphasis on
synchronization and disease propagation.Comment: 52 pages, 5 figures, Survey Pape
Structural constraints in complex networks
We present a link rewiring mechanism to produce surrogates of a network where
both the degree distribution and the rich--club connectivity are preserved. We
consider three real networks, the AS--Internet, the protein interaction and the
scientific collaboration. We show that for a given degree distribution, the
rich--club connectivity is sensitive to the degree--degree correlation, and on
the other hand the degree--degree correlation is constrained by the rich--club
connectivity. In particular, in the case of the Internet, the assortative
coefficient is always negative and a minor change in its value can reverse the
network's rich--club structure completely; while fixing the degree distribution
and the rich--club connectivity restricts the assortative coefficient to such a
narrow range, that a reasonable model of the Internet can be produced by
considering mainly the degree distribution and the rich--club connectivity. We
also comment on the suitability of using the maximal random network as a null
model to assess the rich--club connectivity in real networks.Comment: To appear in New Journal of Physics (www.njp.org
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