1,960 research outputs found
Comment on "Regularizing capacity of metabolic networks"
In a recent paper, Marr, Muller-Linow and Hutt [Phys. Rev. E 75, 041917
(2007)] investigate an artificial dynamic system on metabolic networks. They
find a less complex time evolution of this dynamic system in real networks,
compared to networks of reference models. The authors argue that this suggests
that metabolic network structure is a major factor behind the stability of
biochemical steady states. We reanalyze the same kind of data using a dynamic
system modeling actual reaction kinetics. The conclusions about stability, from
our analysis, are inconsistent with those of Marr et al. We argue that this
issue calls for a more detailed type of modeling
Local interaction scale controls the existence of a non-trivial optimal critical mass in opinion spreading
We study a model of opinion formation where the collective decision of group
is said to happen if the fraction of agents having the most common opinion
exceeds a threshold value, a \textit{critical mass}. We find that there exists
a unique, non-trivial critical mass giving the most efficient convergence to
consensus. In addition, we observe that for small critical masses, the
characteristic time scale for the relaxation to consensus splits into two. The
shorter time scale corresponds to a direct relaxation and the longer can be
explained by the existence of intermediate, metastable states similar to those
found in [P.\ Chen and S.\ Redner, Phys.\ Rev.\ E \textbf{71}, 036101 (2005)].
This longer time-scale is dependent on the precise condition for
consensus---with a modification of the condition it can go away.Comment: 4 pages, 6 figure
Tax evasion dynamics and Zaklan model on Opinion-dependent Network
Within the context of agent-based Monte-Carlo simulations, we study the
well-known majority-vote model (MVM) with noise applied to tax evasion on
Stauffer-Hohnisch-Pittnauer (SHP) networks. To control the fluctuations for tax
evasion in the economics model proposed by Zaklan, MVM is applied in the
neighborhood of the critical noise to evolve the Zaklan model. The
Zaklan model had been studied recently using the equilibrium Ising model. Here
we show that the Zaklan model is robust because this can be studied besides
using equilibrium dynamics of Ising model also through the nonequilibrium MVM
and on various topologies giving the same behavior regardless of dynamic or
topology used here.Comment: 14 page, 4 figure
Exploring the assortativity-clustering space of a network's degree sequence
Nowadays there is a multitude of measures designed to capture different
aspects of network structure. To be able to say if the structure of certain
network is expected or not, one needs a reference model (null model). One
frequently used null model is the ensemble of graphs with the same set of
degrees as the original network. In this paper we argue that this ensemble can
be more than just a null model -- it also carries information about the
original network and factors that affect its evolution. By mapping out this
ensemble in the space of some low-level network structure -- in our case those
measured by the assortativity and clustering coefficients -- one can for
example study how close to the valid region of the parameter space the observed
networks are. Such analysis suggests which quantities are actively optimized
during the evolution of the network. We use four very different biological
networks to exemplify our method. Among other things, we find that high
clustering might be a force in the evolution of protein interaction networks.
We also find that all four networks are conspicuously robust to both random
errors and targeted attacks
A network-based threshold model for the spreading of fads in society and markets
We investigate the behavior of a threshold model for the spreading of fads
and similar phenomena in society. The model is giving the fad dynamics and is
intended to be confined to an underlying network structure. We investigate the
whole parameter space of the fad dynamics on three types of network models. The
dynamics we discover is rich and highly dependent on the underlying network
structure. For some range of the parameter space, for all types of substrate
networks, there are a great variety of sizes and life-lengths of the fads --
what one see in real-world social and economical systems
Third flow component as QGP signal
A review of earlier fluid dynamical calculations with QGP show a softening of
the directed flow while with hadronic matter this effect is absent. The effect
shows up in the reaction plane as enhanced emission which is orthogonal to the
directed flow. Thus, it is not shadowed by the deflected projectile and target.
As both of these flow components are in the reaction plane these form an
enhanced 'elliptic flow' pattern. Recent experimental data at 11 AGeV and above
show the same softening, hinting at QGP formation.Comment: 12 pages, Latex, and 3 figures (.eps), 2 style files (.sty
Network dynamics of ongoing social relationships
Many recent large-scale studies of interaction networks have focused on
networks of accumulated contacts. In this paper we explore social networks of
ongoing relationships with an emphasis on dynamical aspects. We find a
distribution of response times (times between consecutive contacts of different
direction between two actors) that has a power-law shape over a large range. We
also argue that the distribution of relationship duration (the time between the
first and last contacts between actors) is exponentially decaying. Methods to
reanalyze the data to compensate for the finite sampling time are proposed. We
find that the degree distribution for networks of ongoing contacts fits better
to a power-law than the degree distribution of the network of accumulated
contacts do. We see that the clustering and assortative mixing coefficients are
of the same order for networks of ongoing and accumulated contacts, and that
the structural fluctuations of the former are rather large.Comment: to appear in Europhys. Let
The diplomat's dilemma: Maximal power for minimal effort in social networks
Closeness is a global measure of centrality in networks, and a proxy for how
influential actors are in social networks. In most network models, and many
empirical networks, closeness is strongly correlated with degree. However, in
social networks there is a cost of maintaining social ties. This leads to a
situation (that can occur in the professional social networks of executives,
lobbyists, diplomats and so on) where agents have the conflicting objectives of
aiming for centrality while simultaneously keeping the degree low. We
investigate this situation in an adaptive network-evolution model where agents
optimize their positions in the network following individual strategies, and
using only local information. The strategies are also optimized, based on the
success of the agent and its neighbors. We measure and describe the time
evolution of the network and the agents' strategies.Comment: Submitted to Adaptive Networks: Theory, Models and Applications, to
be published from Springe
Nonlocal evolution of weighted scale-free networks
We introduce the notion of globally updating evolution for a class of
weighted networks, in which the weight of a link is characterized by the amount
of data packet transport flowing through it. By noting that the packet
transport over the network is determined nonlocally, this approach can explain
the generic nonlinear scaling between the strength and the degree of a node. We
demonstrate by a simple model that the strength-driven evolution scheme
recently introduced can be generalized to a nonlinear preferential attachment
rule, generating the power-law behaviors in degree and in strength
simultaneously.Comment: 4 pages, 4 figures, final version published in PR
Defining and identifying communities in networks
The investigation of community structures in networks is an important issue
in many domains and disciplines. This problem is relevant for social tasks
(objective analysis of relationships on the web), biological inquiries
(functional studies in metabolic, cellular or protein networks) or
technological problems (optimization of large infrastructures). Several types
of algorithm exist for revealing the community structure in networks, but a
general and quantitative definition of community is still lacking, leading to
an intrinsic difficulty in the interpretation of the results of the algorithms
without any additional non-topological information. In this paper we face this
problem by introducing two quantitative definitions of community and by showing
how they are implemented in practice in the existing algorithms. In this way
the algorithms for the identification of the community structure become fully
self-contained. Furthermore, we propose a new local algorithm to detect
communities which outperforms the existing algorithms with respect to the
computational cost, keeping the same level of reliability. The new algorithm is
tested on artificial and real-world graphs. In particular we show the
application of the new algorithm to a network of scientific collaborations,
which, for its size, can not be attacked with the usual methods. This new class
of local algorithms could open the way to applications to large-scale
technological and biological applications.Comment: Revtex, final form, 14 pages, 6 figure
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