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324
The structure and function of complex networks
 SIAM REVIEW
, 2003
"... 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, ..."
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Cited by 1407 (9 self)
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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 smallworld effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
A neuropsychological theory of multiple systems in category learning
 PSYCHOLOGICAL REVIEW
, 1998
"... A neuropsychological theory is proposed that assumes category learning is a competition between separate verbal and implicit (i.e., procedurallearningbased) categorization systems. The theory assumes that the caudate nucleus is an important component of the implicit system and that the anterior ci ..."
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Cited by 229 (24 self)
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A neuropsychological theory is proposed that assumes category learning is a competition between separate verbal and implicit (i.e., procedurallearningbased) categorization systems. The theory assumes that the caudate nucleus is an important component of the implicit system and that the anterior cingulate and prefrontal cortices are critical to the verbal system. In addition to making predictions for normal human adults, the theory makes specific predictions for children, elderly people, and patients suffering from Parkinson's disease, Huntington's disease, major depression, amnesia, or lesions of the prefrontal cortex. Two separate formal descriptions of the theory are also provided. One describes trialbytrial learning, and the other describes global dynamics. The theory is tested on published neuropsychological data and on category learning data with normal adults.
The dynamics of active categorical perception in an evolved model agent
 ADAPTIVE BEHAVIOR
, 2003
"... ..."
Contraction Analysis of Nonlinear Systems
, 1999
"... Analyzing stability differentially leads to a new perspective on nonlinear dynamic systems Winfried Lohmiller a a, b ..."
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Cited by 101 (33 self)
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Analyzing stability differentially leads to a new perspective on nonlinear dynamic systems Winfried Lohmiller a a, b
Meanfield solution of the smallworld network model
, 2000
"... The smallworld network model is a simple model of the structure of social networks, which simultaneously possesses characteristics of both regular lattices and random graphs. The model consists of a onedimensional lattice with a low density of shortcuts added between randomly selected pairs of poi ..."
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Cited by 62 (6 self)
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The smallworld network model is a simple model of the structure of social networks, which simultaneously possesses characteristics of both regular lattices and random graphs. The model consists of a onedimensional lattice with a low density of shortcuts added between randomly selected pairs of points. These shortcuts greatly reduce the typical path length between any two points on the lattice. We present a meanfield solution for the average path length and for the distribution of path lengths in the model. This solution is exact in the limit of large system size and either large or small number of shortcuts. 1 Social networks, such as networks of friends, have two characteristics which one might imagine were contradictory. First, they show “clustering, ” meaning that two of your friends are far more likely also to be friends of one another than two people chosen from the population at random. Second, they exhibit what has become known as the “smallworld effect,” namely that any two people can establish contact by going through only a short chain of
The simplest walking model: Stability, complexity, and scaling
 ASME Journal of Biomechanical Engineering
, 1998
"... We demonstrate that an irreducibly simple, uncontrolled, 2D, twolink model, vaguely resembling human legs, can walk down a shallow slope, powered only by gravity. This model is the simplest special case of the passivedynamic models pioneered by McGeer (1990a). It has two rigid massless legs hinged ..."
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Cited by 58 (5 self)
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We demonstrate that an irreducibly simple, uncontrolled, 2D, twolink model, vaguely resembling human legs, can walk down a shallow slope, powered only by gravity. This model is the simplest special case of the passivedynamic models pioneered by McGeer (1990a). It has two rigid massless legs hinged at the hip, a pointmass at the hip, and infinitesimal pointmasses at the feet. The feet have plastic (noslip, nobounce) collisions with the slope surface, except during forward swinging, when geometric interference (foot scuffing) is ignored. After nondimensionalizing the governing equations, the model has only one free parameter, the ramp slope γ. This model shows stable walking modes similar to more elaborate models, but allows some use of analytic methods to study its dynamics. The analytic calculations find initial conditions and stability estimates for periodone gait limit cycles. The model exhibits two periodone gait cycles, one of which is stable when 0 <γ<0.015 rad. With increasing γ, stable cycles of higher periods appear, and the walkinglike motions apparently become chaotic through a sequence of period doublings. Scaling laws for the model predict that walking speed is proportional to stance angle, stance angle is proportional to γ 1/3, and that the gravitational power used is proportional to v 4 where v is the velocity along the slope. 1 1
A GameTheoretic Approach to the Simple Coevolutionary Algorithm
 Proceedings of the Sixth International Conference on Parallel Problem Solving from Nature (PPSN VI
"... The fundamental distinction between ordinary evolutionary algorithms (EA) and coevolutionary algorithms lies in the interaction between coevolving entities. We believe that this property is essentially gametheoretic in nature. Using game theory, we describe extensions that allow familiar mixingma ..."
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Cited by 56 (9 self)
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The fundamental distinction between ordinary evolutionary algorithms (EA) and coevolutionary algorithms lies in the interaction between coevolving entities. We believe that this property is essentially gametheoretic in nature. Using game theory, we describe extensions that allow familiar mixingmatrix and Markovchain models of EAs to address coevolutionary algorithm dynamics. We then employ concepts from evolutionary game theory to examine design aspects of conventional coevolutionary algorithms that are poorly understood.
RealTime Techniques for 3D Flow Visualization
, 1998
"... Visualization of three dimensional flow has to overcome a lot of problems to be effective. Among them are occlusion of distant details, lack of directional and depth hints and cluttering. In this paper we present methods which address these problems for realtime graphic representations applicable in ..."
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Cited by 36 (5 self)
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Visualization of three dimensional flow has to overcome a lot of problems to be effective. Among them are occlusion of distant details, lack of directional and depth hints and cluttering. In this paper we present methods which address these problems for realtime graphic representations applicable in virtual environments. We use animated, opacitymapped streamlines as visualization icon for 3D flow visualization. We present a texture mapping technique to keep the level of texture detail along a streamline nearly constant even when the velocity of the flow varies considerably. An algorithm is described which distributes the dashtubes evenly in space. We apply magic lenses and magic boxes as interaction techniques for investigating densly filled areas without overwhelming the observer with visual detail. Implementation details of these methods and their integration in our virtual environment conclude the paper. CR Categories and Subject Descriptors: I.3.3 [Computer Graphics ]: Picture/Ima...
A generalized model of social and biological contagion
 JOURNAL OF THEORETICAL BIOLOGY
, 2005
"... We present a model of contagion that unifies and generalizes existing models of the spread of social influences and microorganismal infections. Our model incorporates individual memory of exposure to a contagious entity (e.g. a rumor or disease), variable magnitudes of exposure (dose sizes), and het ..."
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Cited by 33 (1 self)
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We present a model of contagion that unifies and generalizes existing models of the spread of social influences and microorganismal infections. Our model incorporates individual memory of exposure to a contagious entity (e.g. a rumor or disease), variable magnitudes of exposure (dose sizes), and heterogeneity in the susceptibility of individuals. Through analysis and simulation, we examine in detail the case where individuals may recover from an infection and then immediately become susceptible again (analogous to the socalled SIS model). We identify three basic classes of contagion models which we call epidemic threshold, vanishing critical mass, and critical mass classes, where each class of models corresponds to different strategies for prevention or facilitation. We find that the conditions for a particular contagion model to belong to one of the these three classes depend only on memory length and the probabilities of being infected by one and two exposures, respectively. These parameters are in principle measurable for real contagious influences or entities, thus yielding empirical implications for our model. We also study the case where individuals attain permanent immunity once recovered, finding that epidemics inevitably die out but may be surprisingly persistent when individuals possess memory.