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Nature; 复杂网络;2012特刊

已有 8162 次阅读 2011-12-24 18:10 |系统分类:科研笔记| nature, 复杂科学

                                                               Nature Physics Insight – Complexity

         新千年之后,复杂网络的又一特刊,Nature Physics Insight – Complexity,Insight issue: January 2012 Volume 8, No 1
         这使我们想起:1999年,Barabasi,在Science上发表的“网络网络中标度涌现(Emergence of Scaling in Random Networks)”,几乎同时,在2000年,Barabasi及其合作者在Nature上发表了“复杂网络对错误和攻击的容忍性( Error and attack tolerance of complex networks)“
        这次,在  Nature中,他提出网络科学【network science】,将复杂网络推向新的高潮。该刊在2012年1月出版,现在可阅读到其网络版。该版主要包括:Barabási的评论(Commentary)、3篇回顾综述(Reviews)和1篇最近研究进展。
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Marry Christmas and Happy New Year! 
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           In many large ensembles, the property of the system as a whole cannot be understood from studying the individual entities alone — these ensembles can be made up by neurons in the brain, transport users in traffic networks or data packages in the Internet. The past decade has seen important progress in our fundamental understanding of what such seemingly disparate 'complex systems' have in common; some of these advances are surveyed here.

Commentary: 网络科学【network science】
The network takeoverpp14 – 16

Albert-László Barabási【复杂网络大牛,现哈佛任职】

doi:10.1038/nphys2188

Reductionism, as a paradigm, is expired, and complexity, as a field, is tired. Data-based mathematical models of complex systems are offering a fresh perspective, rapidly developing into a new discipline: network science.

Reviews
Between order and chaos pp17 – 24

James Crutchfield

doi:10.1038/nphys2190

A completely ordered universe is as unexciting as an entirely disordered one. Interesting 'complex' phenomena arise in a middle ground. This article reviews the tools that have been developed to quantify structural complexity and to automatically discover patterns hidden between order and chaos.

Communities, modules and large-scale structure in networks pp25 – 31

M. E. J. Newman

doi:10.1038/nphys2162

Networks have proved to be useful representations of complex systems. Within these networks, there are typically a number of subsystems defined by only a subset of nodes and edges. Detecting these structures often provides important information about the organization and functioning of the overall network. Here, progress towards quantifying medium- and large-scale structures within complex networks is reviewed.

Modelling dynamical processes in complex socio-technical systemspp32 – 39

Alessandro Vespignani

doi:10.1038/nphys2160

Vast amounts of data are available about complex technological systems and how we use them. These data provide the basis not only for mapping out connectivity patterns, but also for the study of dynamical phenomena, including epidemic outbreaks and routing of information through computer networks. This article reviews the fundamental tools for modelling such dynamical processes and discusses a number of applications.

Networks formed from interdependent networks pp40 – 48

Jianxi Gao, Sergey V. Buldyrev, H. Eugene Stanley and Shlomo Havlin

doi:10.1038/nphys2180

Aspects concerning the structure and behaviours of individual networks have been studied intensely in the past decade, but the exploration of interdependent systems in the context of complex networks has started only recently. This article reviews a general framework for modelling the percolation properties of interacting networks and the first results drawn from its study.





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