Degree-dependent intervertex separation in complex networks

We study the mean length $\ell(k)$ of the shortest paths between a vertex of degree $k$ 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, $\ell(k) = A\ln [N/k^{(\gamma-1)/2}] - C k^{\gamma-1}/N + ...$ in a wide range of network sizes. Here $N$ is the number of vertices in the network, $\gamma$ is the degree distribution exponent, and the coefficients $A$ and $C$ depend on a network. We compare this law with a corresponding $\ell(k)$ 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, $\ell(k) \cong A\ln N - C k$. 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 $M$ and the scalar charge $\Sigma$. If one fixes $M$ to a positive value, say $M_0$, and let $\Sigma^2$ take values along the real line we show that this solution exhibits critical behaviour. For $\Sigma^2 >0$ the space-times have eternal naked singularities, for $\Sigma^2 =0$ one has a Schwarzschild black hole of mass $M_0$ and finally for $-M_0^2 \leq \Sigma^2 < 0$ 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