Buscar recursos académicos

Materias / palabras clave: Complex network; Dynamical systems theory; Complex system; Topology (electrical circuits); Physics; Network science; Biological network; Theoretical computer science; Statistical mechanics; Evolving networks; Computer sc...

Complex networks describe a wide range of systems in nature and society. Frequently cited examples include the cell, a network of chemicals linked by chemical reactions, and the Internet, a network of routers and compute...

Inspired by empirical studies of networked systems such as the Internet, social networks, and bio-logical networks, researchers have in recent years developed a variety of techniques and models to help us understand or p...

Materias / palabras clave: Modularity (biology); Complex network; Magnetoencephalography; Computer science; Cognitive science; Connectomics; Diffusion MRI; Graph theory; Human brain; Network science; Neuroscience; Neuroimaging

We propose and study a set of algorithms for discovering community structure in networks-natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they ...

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 com...

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 ...

Many networks of interest in the sciences, including social networks, computer networks, and metabolic and regulatory networks, are found to divide naturally into communities or modules. The problem of detecting and char...

Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are t...

The evolutionary history of a set of taxa is usually represented by a phylogenetic tree, and this model has greatly facilitated the discussion and testing of hypotheses. However, it is well known that more complex evolut...

We develop and analyze low-complexity cooperative diversity protocols that combat fading induced by multipath propagation in wireless networks. The underlying techniques exploit space diversity available through cooperat...

Gephi is an open source software for graph and network analysis. It uses a 3D render engine to display large networks in real-time and to speed up the exploration. A flexible and multi-task architecture brings new possib...

Cellular life depends on a complex web of functional associations between biomolecules. Among these associations, protein-protein interactions are particularly important due to their versatility, specificity and adaptabi...

Convolutional networks are at the core of most state of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains ...

This paper provides a theoretical framework for analysis of consensus algorithms for multi-agent networked systems with an emphasis on the role of directed information flow, robustness to changes in network topology due ...

Previous article Next article An Algorithm for Least-Squares Estimation of Nonlinear ParametersDonald W. MarquardtDonald W. Marquardthttps://doi.org/10.1137/0111030PDFPDF PLUSBibTexSections ToolsAdd to favoritesExport Ci...

Bowling Alone: America’s Declining Social Capital Robert D. Putnam (bio) Many students of the new democracies that have emerged over the past decade and a half have emphasized the importance of a strong and active civi...

Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers...

Preface to the Second Edition. Preface to the First Edition. Acknowledgments for the Second Edition. Acknowledgments for the First Edition. 1. Introduction and Preview. 1.1 Preview of the Book. 2. Entropy, Relative Entro...

Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning alg...

Information processing in the cerebral cortex involves interactions among distributed areas. Anatomical connectivity suggests that certain areas form local hierarchical relations such as within the visual system. Other c...

Materias / palabras clave: governance, network studies, public policy, public management, complexity theory, computational social science; Political science

Materias / palabras clave: complex systems, complex networks, human-machine co-simulation; Science: Mathematics: Instruments and machines: Electronic computers. Computer science; Technology: Engineering (General). Civil engineering (General): Syst...

Materias / palabras clave: complex systems, nonlinear dynamics, chaos, systems science, game theory, complex networks; Science: Mathematics