27,553 research outputs found
Unravelling the size distribution of social groups with information theory on complex networks
The minimization of Fisher's information (MFI) approach of Frieden et al.
[Phys. Rev. E {\bf 60} 48 (1999)] is applied to the study of size distributions
in social groups on the basis of a recently established analogy between scale
invariant systems and classical gases [arXiv:0908.0504]. Going beyond the ideal
gas scenario is seen to be tantamount to simulating the interactions taking
place in a network's competitive cluster growth process. We find a scaling rule
that allows to classify the final cluster-size distributions using only one
parameter that we call the competitiveness. Empirical city-size distributions
and electoral results can be thus reproduced and classified according to this
competitiveness, which also allows to correctly predict well-established
assessments such as the "six-degrees of separation", which is shown here to be
a direct consequence of the maximum number of stable social relationships that
one person can maintain, known as Dunbar's number. Finally, we show that scaled
city-size distributions of large countries follow the same universal
distribution
Universality in survivor distributions: Characterising the winners of competitive dynamics
We investigate the survivor distributions of a spatially extended model of
competitive dynamics in different geometries. The model consists of a
deterministic dynamical system of individual agents at specified nodes, which
might or might not survive the predatory dynamics: all stochasticity is brought
in by the initial state. Every such initial state leads to a unique and
extended pattern of survivors and non-survivors, which is known as an attractor
of the dynamics. We show that the number of such attractors grows exponentially
with system size, so that their exact characterisation is limited to only very
small systems. Given this, we construct an analytical approach based on
inhomogeneous mean-field theory to calculate survival probabilities for
arbitrary networks. This powerful (albeit approximate) approach shows how
universality arises in survivor distributions via a key concept -- the {\it
dynamical fugacity}. Remarkably, in the large-mass limit, the survival
probability of a node becomes independent of network geometry, and assumes a
simple form which depends only on its mass and degree.Comment: 12 pages, 6 figures, 2 table
Feasibility and coexistence of large ecological communities
The role of species interactions in controlling the interplay between the stability of ecosystems and their biodiversity is still not well understood. The ability of ecological communities to recover after small perturbations of the species abundances (local asymptotic stability) has been well studied, whereas the likelihood of a community to persist when the conditions change (structural stability) has received much less attention. Our goal is to understand the effects of diversity, interaction strengths and ecological network structure on the volume of parameter space leading to feasible equilibria. We develop a geometrical framework to study the range of conditions necessary for feasible coexistence. We show that feasibility is determined by few quantities describing the interactions, yielding a nontrivial complexity–feasibility relationship. Analysing more than 100 empirical networks, we show that the range of coexistence conditions in mutualistic systems can be analytically predicted. Finally, we characterize the geometric shape of the feasibility domain, thereby identifying the direction of perturbations that are more likely to cause extinctions
Two universal physical principles shape the power-law statistics of real-world networks
The study of complex networks has pursued an understanding of macroscopic
behavior by focusing on power-laws in microscopic observables. Here, we uncover
two universal fundamental physical principles that are at the basis of complex
networks generation. These principles together predict the generic emergence of
deviations from ideal power laws, which were previously discussed away by
reference to the thermodynamic limit. Our approach proposes a paradigm shift in
the physics of complex networks, toward the use of power-law deviations to
infer meso-scale structure from macroscopic observations.Comment: 14 pages, 7 figure
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