33,479 research outputs found
Degree-dependent intervertex separation in complex networks
We study the mean length of the shortest paths between a vertex of
degree and other vertices in growing networks, where correlations are
essential. In a number of deterministic scale-free networks we observe a
power-law correction to a logarithmic dependence, in a wide range of network
sizes. Here is the number of vertices in the network, is the
degree distribution exponent, and the coefficients and depend on a
network. We compare this law with a corresponding dependence obtained
for random scale-free networks growing through the preferential attachment
mechanism. In stochastic and deterministic growing trees with an exponential
degree distribution, we observe a linear dependence on degree, . We compare our findings for growing networks with those for
uncorrelated graphs.Comment: 8 pages, 3 figure
Emergentism and musicology: an alternative perspective to the understanding of dissonance.
In this paper we develop an approach to musicology within the
discussion of emergentism. First of all, we claim that some theories of
musicology could be insufficient in describing and explaining musical
phenomena when emergent properties are not taken into account. Actually,
musicology usually considers just syntactical elements, structures and
processes and puts only a little emphasis, if any, over perceptual aspects of
human hearing. On the other hand, recent research efforts are currently being
directed towards an understanding of the emergent properties of auditory
perception, especially in fields such as cognitive science. Such research leads
to other views concerning old issues in musicology and could create a fruitful
approach, filling the gap between musicology and auditory perception
Wyman's solution, self-similarity and critical behaviour
We show that the Wyman's solution may be obtained from the four-dimensional
Einstein's equations for a spherically symmetric, minimally coupled, massless
scalar field by using the continuous self-similarity of those equations. The
Wyman's solution depends on two parameters, the mass and the scalar charge
. If one fixes to a positive value, say , and let
take values along the real line we show that this solution exhibits critical
behaviour. For the space-times have eternal naked singularities,
for one has a Schwarzschild black hole of mass and finally
for one has eternal bouncing solutions.Comment: Revtex version, 15pages, 6 figure
Three-feature model to reproduce the topology of citation networks and the effects from authors' visibility on their h-index
Various factors are believed to govern the selection of references in
citation networks, but a precise, quantitative determination of their
importance has remained elusive. In this paper, we show that three factors can
account for the referencing pattern of citation networks for two topics, namely
"graphenes" and "complex networks", thus allowing one to reproduce the
topological features of the networks built with papers being the nodes and the
edges established by citations. The most relevant factor was content
similarity, while the other two - in-degree (i.e. citation counts) and {age of
publication} had varying importance depending on the topic studied. This
dependence indicates that additional factors could play a role. Indeed, by
intuition one should expect the reputation (or visibility) of authors and/or
institutions to affect the referencing pattern, and this is only indirectly
considered via the in-degree that should correlate with such reputation.
Because information on reputation is not readily available, we simulated its
effect on artificial citation networks considering two communities with
distinct fitness (visibility) parameters. One community was assumed to have
twice the fitness value of the other, which amounts to a double probability for
a paper being cited. While the h-index for authors in the community with larger
fitness evolved with time with slightly higher values than for the control
network (no fitness considered), a drastic effect was noted for the community
with smaller fitness
Structure-semantics interplay in complex networks and its effects on the predictability of similarity in texts
There are different ways to define similarity for grouping similar texts into
clusters, as the concept of similarity may depend on the purpose of the task.
For instance, in topic extraction similar texts mean those within the same
semantic field, whereas in author recognition stylistic features should be
considered. In this study, we introduce ways to classify texts employing
concepts of complex networks, which may be able to capture syntactic, semantic
and even pragmatic features. The interplay between the various metrics of the
complex networks is analyzed with three applications, namely identification of
machine translation (MT) systems, evaluation of quality of machine translated
texts and authorship recognition. We shall show that topological features of
the networks representing texts can enhance the ability to identify MT systems
in particular cases. For evaluating the quality of MT texts, on the other hand,
high correlation was obtained with methods capable of capturing the semantics.
This was expected because the golden standards used are themselves based on
word co-occurrence. Notwithstanding, the Katz similarity, which involves
semantic and structure in the comparison of texts, achieved the highest
correlation with the NIST measurement, indicating that in some cases the
combination of both approaches can improve the ability to quantify quality in
MT. In authorship recognition, again the topological features were relevant in
some contexts, though for the books and authors analyzed good results were
obtained with semantic features as well. Because hybrid approaches encompassing
semantic and topological features have not been extensively used, we believe
that the methodology proposed here may be useful to enhance text classification
considerably, as it combines well-established strategies
Accelerating universes driven by bulk particles
We consider our universe as a 3d domain wall embedded in a 5d dimensional
Minkowski space-time. We address the problem of inflation and late time
acceleration driven by bulk particles colliding with the 3d domain wall. The
expansion of our universe is mainly related to these bulk particles. Since our
universe tends to be permeated by a large number of isolated structures, as
temperature diminishes with the expansion, we model our universe with a 3d
domain wall with increasing internal structures. These structures could be
unstable 2d domain walls evolving to fermi-balls which are candidates to cold
dark matter. The momentum transfer of bulk particles colliding with the 3d
domain wall is related to the reflection coefficient. We show a nontrivial
dependence of the reflection coefficient with the number of internal dark
matter structures inside the 3d domain wall. As the population of such
structures increases the velocity of the domain wall expansion also increases.
The expansion is exponential at early times and polynomial at late times. We
connect this picture with string/M-theory by considering BPS 3d domain walls
with structures which can appear through the bosonic sector of a
five-dimensional supergravity theory.Comment: To appear in Phys. Rev. D, 16 pages, 3 eps figures, minor changes and
references adde
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