Robustness of journal rankings by network flows with different amounts of memory

As the number of scientific journals has multiplied, journal rankings have become increasingly important for scientific decisions. From submissions and subscriptions to grants and hirings, researchers, policy makers, and funding agencies make important decisions with influence from journal rankings such as the ISI journal impact factor. Typically, the rankings are derived from the citation network between a selection of journals and unavoidably depend on this selection. However, little is known about how robust rankings are to the selection of included journals. Here we compare the robustness of three journal rankings based on network flows induced on citation networks. They model pathways of researchers navigating scholarly literature, stepping between journals and remembering their previous steps to different degree: zero-step memory as impact factor, one-step memory as Eigenfactor, and two-step memory, corresponding to zero-, first-, and second-order Markov models of citation flow between journals. We conclude that higher-order Markov models perform better and are more robust to the selection of journals. Whereas our analysis indicates that higher-order models perform better, the performance gain for the second-order Markov model comes at the cost of requiring more citation data over a longer time period.Comment: 9 pages, 5 figure

The segments method

Se reporta el uso de segmentos como dominio de integración para los métodos libres de mallas de tipo Petrov-Galerkin Local (MLPG). El procedimiento acarrea ventajas en el tratamiento de dominios con forma geométrica irregular, circunda el problema de la precisión numérica en la cuadratura y permite de forma simple traspasar un número arbitrario de derivadas de la función de forma a la función de ponderación. Este trabajo describe el procedimiento algebraico necesario. Además hace referencia al estudio experimental de errores que se hizo para el presente método en casos de prueba bidimensionales; con el objetivo de constatar la estabilidad y precisión del mismo.Peer Reviewe

Narrowing the gap between network models and real complex systems

Simple network models that focus only on graph topology or, at best, basic interactions are often insufficient to capture all the aspects of a dynamic complex system. In this thesis, I explore those limitations, and some concrete methods of resolving them. I argue that, in order to succeed at interpreting and influencing complex systems, we need to take into account  slightly more complex parts, interactions and information flows in our models.This thesis supports that affirmation with five actual examples of applied research. Each study case takes a closer look at the dynamic of the studied problem and complements the network model with techniques from information theory, machine learning, discrete maths and/or ergodic theory. By using these techniques to study the concrete dynamics of each system, we could obtain interesting new information. Concretely, we could get better models of network walks that are used on everyday applications like journal ranking. We could also uncover asymptotic characteristics of an agent-based information propagation model which we think is the basis for things like belief propaga-tion or technology adoption on society. And finally, we could spot associations between antibiotic resistance genes in bacterial populations, a problem which is becoming more serious every day

Narrowing the gap between network models and real complex systems

Simple network models that focus only on graph topology or, at best, basic interactions are often insufficient to capture all the aspects of a dynamic complex system. In this thesis, I explore those limitations, and some concrete methods of resolving them. I argue that, in order to succeed at interpreting and influencing complex systems, we need to take into account  slightly more complex parts, interactions and information flows in our models.This thesis supports that affirmation with five actual examples of applied research. Each study case takes a closer look at the dynamic of the studied problem and complements the network model with techniques from information theory, machine learning, discrete maths and/or ergodic theory. By using these techniques to study the concrete dynamics of each system, we could obtain interesting new information. Concretely, we could get better models of network walks that are used on everyday applications like journal ranking. We could also uncover asymptotic characteristics of an agent-based information propagation model which we think is the basis for things like belief propaga-tion or technology adoption on society. And finally, we could spot associations between antibiotic resistance genes in bacterial populations, a problem which is becoming more serious every day

The segments method

Se reporta el uso de segmentos como dominio de integración para los métodos libres de mallas de tipo Petrov-Galerkin Local (MLPG). El procedimiento acarrea ventajas en el tratamiento de dominios con forma geométrica irregular, circunda el problema de la precisión numérica en la cuadratura y permite de forma simple traspasar un número arbitrario de derivadas de la función de forma a la función de ponderación. Este trabajo describe el procedimiento algebraico necesario. Además hace referencia al estudio experimental de errores que se hizo para el presente método en casos de prueba bidimensionales; con el objetivo de constatar la estabilidad y precisión del mismo.Peer Reviewe

Técnica de visualización científica para datos escalares discretos

Peer Reviewe

Técnica de visualización científica para datos escalares discretos

Peer Reviewe

Utilización de árboles de cubrimiento para interpolar usando funciones de base radial enfocado a la visualización científica de grandes volúmenes de datos

En este presente trabajo se desarrolla un método para interpolar grandes volúmenes de datos esparcidos, dirigido principalmente a los resultados de la aplicación de Métodos libres de Malla, Métodos de Punto y de Partículas. En el mismo se hace uso de las funciones de base radial con alcance local como funciones interpoladoras. Se utilizan los árboles de cubrimiento como la estructura de datos que permite acelerar la localización de datos que influyen para interpolar los valores en un nuevo punto, lo cual agiliza la aplicación de técnicas de visualización científicas para la generación de imágenes a partir de grandes volúmenes de datos provenientes de la aplicación de Métodos libres de Malla, Métodos de Puntos, y de Partículas, en la resolución de diversos modelos de la física-matemática. Como ejemplo se muestran los resultados obtenidos tras la utilización de dicho método, empleando la función interpoladora de Shepard de alcance local.Peer Reviewe