Duncan J. Watts

Bio: Duncan J. Watts is an academic researcher from University of Pennsylvania. The author has contributed to research in topics: Randomness & Small-world network. The author has an hindex of 62, co-authored 146 publications receiving 83816 citations. Previous affiliations of Duncan J. Watts include Cornell University & Microsoft.

Renormalization Group Analysis of the Small-World Network Model

Abstract: We study the small-world network model, which mimics the transition between regular-lattice and random-lattice behavior in social networks of increasing size. We contend that the model displays a normal continuous phase transition with a divergent correlation length as the degree of randomness tends to zero. We propose a real-space renormalization group transformation for the model and demonstrate that the transformation is exact in the limit of large system size. We use this result to calculate the exact value of the single critical exponent for the system, and to derive the scaling form for the average number of "degrees of separation" between two nodes on the network as a function of the three independent variables. We confirm our results by extensive numerical simulation. Appears in Phys. Lett. A 263, 341-346 (1999).

Collective Problem-Solving of Groups Across Tasks of Varying Complexity

Abstract: As organizations gravitate to group-based structures, the problem of improving performance through judicious selection of group members has preoccupied scientists and managers alike. However, it remains poorly understood under what conditions groups outperform comparable individuals, which individual attributes best predict group performance, or how task complexity mediates these relationships. Here we describe a novel two-phase experiment in which individuals were evaluated on a series of tasks of varying complexity; then randomly assigned to solve similar tasks either in groups of different compositions or as individuals. We describe two main sets of findings. First, while groups are more efficient than individuals and comparable “nominal group” when the task is complex, this relationship is reversed when the task is simple. Second, we find that average skill level dominates all other factors combined, including social perceptiveness, skill diversity, and diversity of cognitive style. Our findings illustrate the utility of a “solution-oriented” approach to identifying principles of collective performance.

Comparing Estimates of News Consumption from Survey and Passively collected Behavioral Data

Abstract: Surveys are a vital tool for understanding public opinion and knowledge, but they can also yield biased estimates of behavior. Here we explore a popular and important behavior that is frequently measured in public opinion surveys: news consumption. Previous studies have shown that television news consumption is consistently overreported in surveys relative to passively collected behavioral data. We validate these earlier findings, showing that they continue to hold despite large shifts in news consumption habits over time, while also adding some new nuance regarding question wording. We extend these findings to survey reports of online and social media news consumption, with respect both to levels and trends. Third, we demonstrate the usefulness of passively collected data for measuring a quantity such as “consuming news” for which different researchers might reasonably choose different definitions. Finally, recognizing that passively collected data suffers from its own limitations, we outline a framework for using a mix of passively collected behavioral and survey-generated attitudinal data to accurately estimate consumption of news and related effects on public opinion and knowledge, conditional on media consumption.

Ideas innovadoras para 2007

Abstract: Duncan J. Watts sostiene que son las personas comunes y no los ?influenciadores? quienes conducen las epidemias sociales. Yoshito Hori predice que los jovenes emprendedores de Japon opacaran a los de China e India. Frederic Dalsace, Coralie Damay y David Dubois proponen marcas que ?al igual que Harry Potter? maduren con sus clientes. Michael Schrage revela el valor oculto de ecuaciones olvidadas hace mucho tiempo. Harry Hutson y Barbara Perry vuelven a colocar a la esperanza en el repertorio ejecutivo. Eric von Hippel destaca a Dinamarca, donde ?la innovacion centrada en el usuario? es una prioridad nacional. Linda Stone detecta un rechazo contra la adiccion a los celulares y BlackBerry. Michael C. Mankins sugiere donde colocar el exceso de efectivo. Ap Dijksterhuis reafirma el valor de consultar una decision con la almohada. Robert G. Eccles, Liv Watson y Mike Willis reportan sobre el nuevo estandar que hara que la informacion financiera sea sustancialmente mas facil de generar, agregar y analizar. Geoffrey B. West cuestiona que las funciones de innovacion mas pequenas sean las mas fecundas. Karen Fraser advierte sobre los consumidores leales que pueden irse por razones eticas. Philip Longman predice el regreso de grandes familias patriarcales y sus efectos en la estrategia de marketing. Rashi Glazer ilustra las implicaciones socioculturales y de negocios de la nanotecnologia. Yoko Ishikura urge a las empresas globales a ?pensar localmente?. Klaus Kleinfeld y Erich Reinhardt exploran la convergencia de la tecnologia de imagen y la biotecnologia y sus enormes beneficios para la atencion medica. Christopher Meyer sugiere enfocarse en que es lo que quiere de su red antes de construir la plataforma. Charles R. Morris sostiene que los costos de salud en EE.UU. estan bajando; es el gasto lo que esta subiendo. Clay Shirky muestra por que los proyectos de fuente abierta tienen exito gracias a sus fracasos. David Weinberger afirma que la responsabilizacion se ha transformado en un ?responsabilismo? supersticioso.

Six Degrees: The New Science of Networks

Abstract: A central objective of this course is to help you to think about real-world social, cultural, economic, organizational, ecological, and technological problems in a different way. To this end, instead of a term paper, you will be required to submit at the end of the semester, a "scrap book" of annotated clippings from the media. This project is not meant to be arduous—in fact, it is intended to be fun—and you can approach it in many different ways. The main objectives are (a) to encourage you to keep abreast of current events, as well as contemporary ideas and trends; and (b) to help you take the concepts of the course out of the classroom and use them to interpret the world around you.

Emergence of Scaling in Random Networks

Abstract: Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Statistical mechanics of complex networks

Abstract: The emergence of order in natural systems is a constant source of inspiration for both physical and biological sciences. While the spatial order characterizing for example the crystals has been the basis of many advances in contemporary physics, most complex systems in nature do not offer such high degree of order. Many of these systems form complex networks whose nodes are the elements of the system and edges represent the interactions between them. Traditionally complex networks have been described by the random graph theory founded in 1959 by Paul Erdohs and Alfred Renyi. One of the defining features of random graphs is that they are statistically homogeneous, and their degree distribution (characterizing the spread in the number of edges starting from a node) is a Poisson distribution. In contrast, recent empirical studies, including the work of our group, indicate that the topology of real networks is much richer than that of random graphs. In particular, the degree distribution of real networks is a power-law, indicating a heterogeneous topology in which the majority of the nodes have a small degree, but there is a significant fraction of highly connected nodes that play an important role in the connectivity of the network. The scale-free topology of real networks has very important consequences on their functioning. For example, we have discovered that scale-free networks are extremely resilient to the random disruption of their nodes. On the other hand, the selective removal of the nodes with highest degree induces a rapid breakdown of the network to isolated subparts that cannot communicate with each other. The non-trivial scaling of the degree distribution of real networks is also an indication of their assembly and evolution. Indeed, our modeling studies have shown us that there are general principles governing the evolution of networks. Most networks start from a small seed and grow by the addition of new nodes which attach to the nodes already in the system. This process obeys preferential attachment: the new nodes are more likely to connect to nodes with already high degree. We have proposed a simple model based on these two principles wich was able to reproduce the power-law degree distribution of real networks. Perhaps even more importantly, this model paved the way to a new paradigm of network modeling, trying to capture the evolution of networks, not just their static topology.

The Structure and Function of Complex Networks

Abstract: Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.

Community structure in social and biological networks

Abstract: A number of recent studies have focused on the statistical properties of networked systems such as social networks and the Worldwide Web. Researchers have concentrated particularly on a few properties that seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this article, we highlight another property that is found in many networks, the property of community structure, in which network nodes are joined together in tightly knit groups, between which there are only looser connections. We propose a method for detecting such communities, built around the idea of using centrality indices to find community boundaries. We test our method on computer-generated and real-world graphs whose community structure is already known and find that the method detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well known—a collaboration network and a food web—and find that it detects significant and informative community divisions in both cases.