Computer Virus Propagation in a Network Organization: The Interplay between Social and Technological Networks

This paper proposes a holistic view of a network organization's computing environment to examine computer virus propagation patterns. We empirically examine a large-scale organizational network consisting of both social network and technological network. By applying information retrieval techniques, we map nodes in the social network to nodes in the technological network to construct the composite network of the organization. We apply social network analysis to study the topologies of social and technological networks in this organization. We statistically test the impact of the interplay between social and technological network on computer virus propagation using a susceptible-infective-recovered epidemic process. We find that computer viruses propagate faster but reach lower level of infection through technological network than through social network, and viruses propagate the fastest and reach the highest level of infection through the composite network. Overlooking the interplay of social network and technological network underestimates the virus propagation speed and the scale of infection

Information Spreading in Stationary Markovian Evolving Graphs

Markovian evolving graphs are dynamic-graph models where the links among a fixed set of nodes change during time according to an arbitrary Markovian rule. They are extremely general and they can well describe important dynamic-network scenarios. We study the speed of information spreading in the "stationary phase" by analyzing the completion time of the "flooding mechanism". We prove a general theorem that establishes an upper bound on flooding time in any stationary Markovian evolving graph in terms of its node-expansion properties. We apply our theorem in two natural and relevant cases of such dynamic graphs. "Geometric Markovian evolving graphs" where the Markovian behaviour is yielded by "n" mobile radio stations, with fixed transmission radius, that perform independent random walks over a square region of the plane. "Edge-Markovian evolving graphs" where the probability of existence of any edge at time "t" depends on the existence (or not) of the same edge at time "t-1". In both cases, the obtained upper bounds hold "with high probability" and they are nearly tight. In fact, they turn out to be tight for a large range of the values of the input parameters. As for geometric Markovian evolving graphs, our result represents the first analytical upper bound for flooding time on a class of concrete mobile networks.Comment: 16 page

Modeling Tuberculosis spreading for the evaluation of new vaccines

La Tuberculosis (TB) es una enfermedad infecciosa que causa más de 10 millones de nuevos casos y 1.5 millones de muertes al año. La actual vacuna, BCG, no es capaz de proporcionar una eficacia consistente, y por ello existe la imperiosa necesidad de desarrollar nuevas vacunas. En este contexto, la modelización matemática puede jugar un papel clave en la evaluación y comparación de estas nuevas vacunas con el propósito final de asistir en la elaboración de políticas y optimización de las estrategias de vacunación. El objetivo de esta tesis es la creación de un modelo apropiado para la evaluación del impacto de estas nuevas vacunas. Para ello centramos nuestros esfuerzos en dos vertientes distintas: la modelización de la propagación de la Tuberculosis per se, y la parametrización de estas nuevas vacunas para su evaluación con estos nuevos modelos. En lo que se refiere a la modelización de la propagación de la enfermedad, los principales avances propuestos en esta tesis están relacionados con la estructura de edad de las poblaciones. Específicamente implementaremos, por primera vez en un modelo de propagación de TB, contactos dependientes de la edad y la evolución temporal de las pirámides demográficas. Así, comenzamos la tesis estudiando el problema teórico de implementar patrones de contacto empíricos dependientes de la edad en distintas esructuras demográficas. Es una tendencia actual en epidemiología utilizar estos patrones de contacto por edades heterogéneos, superando así la asunción clásica de mezcla homogénea. Sin embargo, estos patrones de contacto han sido medidos en poco más de una decena de localizaciones diferentes, y queda pendiente la cuestión de hasta que punto unos patrones de contacto que corresponden a una población específica son transferibles a otra localización diferente. En esta tesis estudiamos distintos métodos para proyectar matrices de contacto de una población a otra con distinta estructura demográfica, y analizaremos las difierencias que existen en los patrones de contacto de distintos países. Este estudio es fundamental para la construcción de nuestro modelo en el que pretendemos acoplar patrones de contacto por edades con una evolución temporal de la estructura por edades de la población, de forma que deberemos adaptar esas matrices de contacto por edades en cada paso temporal. En el siguiente capítulo desarrollamos un modelo de propagación de la Tuberculosis en el que integramos una gran cantidad de datos sobre una Historia Natural para la enfermedad con 19 estados diferentes (incluyendo dos estados de latencia, tres tipos distintos de enfermedad con distinta infecciosidad, y distintos resultados del tratamiento). Así, nuestro modelo utilizará como input, datos de incidencia y mortalidad específicos de cada país, parámetros epidemiológicos con dependencia de la edad obtenidos de diferentes estudios, y, como ya hemos avanzado, incorporamos por primera vez en el campo proyecciones demográficas y matrices de contacto por edades. En este trabajo, identificamos sesgos substanciales arraigados en una descripción inadequada de estos aspectos, a nivel tanto de incidencia y mortalidad agregadas como en su distribución por edades. Una vez que la base del modelo de propagación de Tuberculosis está establecida, el siguiente paso es el estudio de la parametrización de los efectos de la vacuna en el contexto del modelo introducido. Aunque nuestro objetivo último es estudiar el impacto final que tendrán hipotéticas nuevas vacunas, es fundamental obtener toda la información posible de la actual vacuna BCG, ya que muchos de los efectos y problemas que tiene esta vacuna podrían darse también de forma inevitable en las nuevas vacunas. En concreto, sobre BCG estudiaremos la serie de ensayos clínicos BCG-REVAC, diseñados para intentar discernir qué mecanismo, producido por exposición previa a micobacterias (enmascaramiento y/o bloqueo), está detrás de la variabilidad en la eficacia de BCG medida en distintos lugares. Aunque esta discusión ya había sido realizada cualitativamente, en esta tesis proponemos varios modelos matemáticos (con bloqueo, con enmascaramiento y con los dos efectos), comprobamos cúal de ellos ajusta mejor a los datos obtenidos por esta serie de ensayos clínicos y cuantificamos estos efectos. A continuación, estudiamos el diseño de ensayos clínicos que se implementarán sobre las nuevas vacunas y que nos proveerán de toda la información posible para su evaluación con un modelo de propagación, ya que debido a la falta de correlaciones de protección de Tuberculosis, los ensayos clínicos son la única forma de determinar la eficacia de una vacuna. La formulación clásica de estos ensayos clínicos ofrece una parametrización muy limitada de la vacuna. En concreto, ofrece un único dato de eficacia contra enfermedad, cuando en realidad existen múltiples mecanismos con los que una vacuna puede interrumpir el ciclo del patógeno, y que permanecen indistinguibles en un ensayo clínico lo que provoca grandes incertidumbre en la posterior evaluación de impacto. Estudiaremos un nuevo diseño para estos ensayos clínicos, capaz de ofrecer una parametrización más completa de la vacuna. Finalmente, una vez que ya hemos desarrollado un nuevo modelo de propagación de Tuberculosis y hemos estudiado en detalle la descripción de las vacunas en este contexto, evaluamos diferentes vacunas hipotéticas. Nos centraremos en el debate actual sobre la edad óptima de vacunación.<br /

Complex Networks and Symmetry I: A Review

In this review we establish various connections between complex networks and symmetry. While special types of symmetries (e.g., automorphisms) are studied in detail within discrete mathematics for particular classes of deterministic graphs, the analysis of more general symmetries in real complex networks is far less developed. We argue that real networks, as any entity characterized by imperfections or errors, necessarily require a stochastic notion of invariance. We therefore propose a definition of stochastic symmetry based on graph ensembles and use it to review the main results of network theory from an unusual perspective. The results discussed here and in a companion paper show that stochastic symmetry highlights the most informative topological properties of real networks, even in noisy situations unaccessible to exact techniques.Comment: Final accepted versio

Complex networks approach to modeling online social systems. The emergence of computational social science

This thesis is devoted to quantitative description, analysis, and modeling of complex social systems in the form of online social networks. Statistical patterns of the systems under study are unveiled and interpreted using concepts and methods of network science, social network analysis, and data mining. A long-term promise of this research is that predicting the behavior of complex techno-social systems will be possible in a way similar to contemporary weather forecasting, using statistical inference and computational modeling based on the advancements in understanding and knowledge of techno-social systems. Although the subject of this study are humans, as opposed to atoms or molecules in statistical physics, the availability of extremely large datasets on human behavior permits the use of tools and techniques of statistical physics. This dissertation deals with large datasets from online social networks, measures statistical patterns of social behavior, and develops quantitative methods, models, and metrics for complex techno-social systemsLa presente tesis está dedicada a la descripción, análisis y modelado cuantitativo de sistemas complejos sociales en forma de redes sociales en internet. Mediante el uso de métodos y conceptos provenientes de ciencia de redes, análisis de redes sociales y minería de datos se descubren diferentes patrones estadísticos de los sistemas estudiados. Uno de los objetivos a largo plazo de esta línea de investigación consiste en hacer posible la predicción del comportamiento de sistemas complejos tecnológico-sociales, de un modo similar a la predicción meteorológica, usando inferencia estadística y modelado computacional basado en avances en el conocimiento de los sistemas tecnológico-sociales. A pesar de que el objeto del presente estudio son seres humanos, en lugar de los átomos o moléculas estudiados tradicionalmente en la física estadística, la disponibilidad de grandes bases de datos sobre comportamiento humano hace posible el uso de técnicas y métodos de física estadística. En el presente trabajo se utilizan grandes bases de datos provenientes de redes sociales en internet, se miden patrones estadísticos de comportamiento social, y se desarrollan métodos cuantitativos, modelos y métricas para el estudio de sistemas complejos tecnológico-sociales

Do Social Networks Solve Information Problems for Peer-to-Peer Lending?Evidence from Prosper.com

This paper studies peer-to-peer (p2p) lending on the Internet. Prosper.com, the first p2p lending website in the US, matches individual lenders and borrowers for unsecured consumer loans. Using transaction data from June 1, 2006 to July 31, 2008, we examine what information problems exist on Prosper and whether social networks help alleviate the information problems. As we expect, data identifies three information problems on Prosper.com. First, Prosper lenders face extra adverse selection because they observe categories of credit grades rather than the actual credit scores. This selection is partially offset when Prosper posts more detailed credit information on the website. Second, many Prosper lenders have made mistakes in loan selection but they learn vigorously over time. Third, as Stiglitz and Weiss (1981) predict, a higher interest rate can imply lower rate of return because higher interest attracts lower quality borrowers. Micro-finance theories argue that social networks may identify good risks either because friends and colleagues observe the intrinsic type of borrowers ex ante or because the monitoring within social networks provides a stronger incentive to pay off loans ex post. We find evidence both for and against this argument. For example, loans with friend endorsements and friend bids have fewer missed payments and yield significantly higher rates of return than other loans. On the other hand, the estimated returns of group loans are significantly lower than those of non-group loans. That being said, the return gap between group and non-group loans is closing over time. This convergence is partially due to lender learning and partially due to Prosper eliminating group leader rewards which motivated leaders to fund lower quality loans in order to earn the rewards

Networks, Epidemics and Collective Behavior: from Physics to Data Science

In the final quarter of the XX century the classical reductionist approach that had been driving the development of physics was questioned. Instead, it was proposed that systems were arranged in hierarchies so that the upper level had to convey to the rules of the lower level, but at the same time it could also exhibit its own laws that could not be inferred from the ones of its fundamental constituents. This observation led to the creation of a new field known as complex systems. This novel view was, however, not restricted to purely physical systems. It was soon noticed that very different systems covering a huge array of fields, from ecology to sociology or economics, could also be analyzed as complex systems. Furthermore, it allowed physicists to contribute with their knowledge and methods in the development of research in those areas. In this thesis we tackle problems covering three areas of complex systems: networks, which are one of the main mathematical tools used to study complex systems; epidemic spreading, which is one of the fields in which the application of a complex systems perspective has been more successful; and the study of collective behavior, which has attracted a lot of attention since data from human behavior in huge amounts has been made available thanks to social networks. In fact, data is also the main driver of our discussion of the other two areas. In particular, we use novel sources of data to challenge some of the classical assumptions that have been made in the study of networks as well as in the development of models of epidemic spreading. In the case of networks, the problem of null models is addressed using tools coming from statistical physics. We show that anomalies in networks can be just a consequence of model oversimplification. Then, we extend the framework to generate contact networks for the spreading of diseases in populations in which both the contact structure and the age distribution of the population are important. Next, we follow the historical development of mathematical epidemiology and revisit the assumptions that were made when there was no data about the real behavior of this kind of systems. We show that one of the most important quantities used in this kind of studies, the basic reproduction number, is not properly defined for real systems. Similarly, we extend the theoretical framework of epidemic spreading on directed networks to multilayer systems. Furthermore, we show that the challenge of incorporating data to models is not only restricted to the problem of obtaining it, but that it is also really important to be aware of its characteristics to do it properly.Lastly, we conclude the thesis studying two examples of collective behavior using data extracted from online systems. We do so using techniques that were originally developed for other purposes, such as earthquake prediction. Yet, we demonstrate that they can also be used to study this new type of systems. Furthermore, we show that, despite their unique characteristics, they possess properties similar to the ones that have been observed in the offline world. This not only means that modern societies are intertwined with the online world, but it also signals that if we aim to understand socio-technical systems a holistic approach, as the one proposed by complex systems, is indispensable.<br /