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相关论文: Cognitive Maps of Complex Systems Show Hierarchica…

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Many networks in natural and human-made systems exhibit scale-free properties and are small worlds. Now we show that people's understanding of complex systems in their cognitive maps also follow a scale-free topology. People focus on a few…

其他定量生物学 · 定量生物学 2007-05-23 Uygar Ozesmi

Recent developments in graph theoretic analysis of complex networks have led to deeper understanding of brain networks. Many complex networks show similar macroscopic behaviors despite differences in the microscopic details. Probably two…

神经元与认知 · 定量生物学 2021-03-11 Moo K. Chung

Scale-free networks are abundant in nature and society, describing such diverse systems as the world wide web, the web of human sexual contacts, or the chemical network of a cell. All models used to generate a scale-free topology are…

统计力学 · 物理学 2009-11-07 Albert-Laszlo Barabasi , Erzsebet Ravasz , Tamas Vicsek

Hierarchical networks actually have many applications in the real world. Firstly, we propose a new class of hierarchical networks with scale-free and fractal structure, which are the networks with triangles compared to traditional…

组合数学 · 数学 2022-11-23 Jia-Bao Liu , Yan Bao , Wu-Ting Zheng

A central claim in modern network science is that real-world networks are typically "scale free," meaning that the fraction of nodes with degree $k$ follows a power law, decaying like $k^{-\alpha}$, often with $2 < \alpha < 3$. However,…

物理与社会 · 物理学 2019-03-19 Anna D. Broido , Aaron Clauset

Most of various large-size complex systems in nature and society can be well described as complex networks (graphs) to better understand the evolutional mechanisms and dynamical functions behind themselves. Of some part follow scale-free…

物理与社会 · 物理学 2023-08-08 Fei Ma

We study a recently introduced class of scale-free networks showing a high clustering coefficient and non-trivial connectivity correlations. We find that the connectivity probability distribution strongly depends on the fine details of the…

A large number of complex networks, both natural and artificial, share the presence of highly heterogeneous, scale-free degree distributions. A few mechanisms for the emergence of such patterns have been suggested, optimization not being…

统计力学 · 物理学 2009-11-07 S. Valverde , R. Ferrer i Cancho , R. V. Sole

Many real-world complex systems such as social, biological, information as well as technological systems results of a decentralized and unplanned evolution which leads to a common structuration. Irrespective of their origin, these so-called…

社会与信息网络 · 计算机科学 2013-05-03 Chantal Cherifi , Jean-François Santucci

Structure entails function and thus a structural description of the brain will help to understand its function and may provide insights into many properties of brain systems, from their robustness and recovery from damage, to their dynamics…

神经元与认知 · 定量生物学 2008-08-27 Marcus Kaiser , Robert Martin , Peter Andras , Malcolm P. Young

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…

无序系统与神经网络 · 物理学 2015-06-25 Albert-Laszlo Barabasi , Reka Albert

Methods connecting dynamical systems and graph theory have attracted increasing interest in the past few years, with applications ranging from a detailed comparison of different kinds of dynamics to the characterisation of empirical data.…

统计力学 · 物理学 2018-01-18 Marcello A. Budroni , Andrea Baronchelli , Romualdo Pastor-Satorras

The concept of scale-free networks has been widely applied across natural and physical sciences. Many claims are made about the properties of these networks, even though the concept of scale-free is often vaguely defined. We present tools…

适应与自组织系统 · 物理学 2013-10-18 Kevin Judd , Michael Small , Thomas Stemler

To explore the relation between network structure and function, we studied the computational performance of Hopfield-type attractor neural nets with regular lattice, random, small-world and scale-free topologies. The random net is the most…

无序系统与神经网络 · 物理学 2009-11-10 Patrick N. Mcgraw , Michael Menzinger

Many real networks in nature and society share two generic properties: they are scale-free and they display a high degree of clustering. We show that these two features are the consequence of a hierarchical organization, implying that small…

统计力学 · 物理学 2009-11-07 Erzsebet Ravasz , Albert-Laszlo Barabasi

Many complex systems--from social and communication networks to biological networks and the Internet--are thought to exhibit scale-free structure. However, prevailing explanations rely on the constant addition of new nodes, an assumption…

适应与自组织系统 · 物理学 2022-11-10 Christopher W. Lynn , Caroline M. Holmes , Stephanie E. Palmer

In the past few years, several studies have explored the topology of interactions in different complex systems. Areas of investigation span from biology to engineering, physics and the social sciences. Although having different microscopic…

无序系统与神经网络 · 物理学 2009-11-11 M. Bartolozzi , D. B. Leinweber , T. Surungan , A. W. Thomas , A. G. Williams

A large computer program is typically divided into many hundreds or even thousands of smaller units, whose logical connections define a network in a natural way. This network reflects the internal structure of the program, and defines the…

无序系统与神经网络 · 物理学 2009-11-10 Alessandro P. S. de Moura , Ying-Cheng Lai , Adilson E. Motter

Random networks with complex topology are common in Nature, describing systems as diverse as the world wide web or social and business networks. Recently, it has been demonstrated that most large networks for which topological information…

无序系统与神经网络 · 物理学 2016-08-31 Albert-Laszlo Barabasi , Reka Albert , Hawoong Jeong

We give exact relations for certain types of the hierarchic fractal structures. In the blatant distinction from regular networks of the "small world" (SW) topology [1], regular fractal networks manifests the logarithmic dependence of the…

无序系统与神经网络 · 物理学 2007-05-23 Gregory Surdutovich , Vladimir Gol'dshtein , Gennady Koganov
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