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相关论文: Highly clustered scale-free networks

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We generalize the scale-free network model of Barab\`asi and Albert [Science 286, 509 (1999)] by proposing a class of stochastic models for scale-free interdependent networks in which interdependent nodes are not randomly connected but…

物理与社会 · 物理学 2015-06-11 Boris Podobnik , Davor Horvatic , Mark Dickison , H. Eugene Stanley

A new mechanism leading to scale-free networks is proposed in this letter. It is shown that in many cases of interest, the connectivity power-law behavior is neither related to dynamical properties nor to preferential attachment. Instead,…

无序系统与神经网络 · 物理学 2007-05-23 G. Caldarelli , A. Capocci , P. De Los Rios , M. A. Munoz

Real-world networks often have power-law degrees and scale-free properties such as ultra-small distances and ultra-fast information spreading. In this paper, we study a third universal property: three-point correlations that suppress the…

社会与信息网络 · 计算机科学 2017-11-01 Clara Stegehuis , Remco van der Hofstad , Johan S. H. van Leeuwaarden , A. J. E. M Janssen

In this paper we describe the emergence of scale-free degree distributions from statistical mechanics principles. We define an energy associated to a degree sequence as the logarithm of the number of indistinguishable simple networks it is…

统计力学 · 物理学 2007-05-23 Ginestra Bianconi

Real-world networks are generally claimed to be scale-free, meaning that the degree distributions follow the classical power-law, at least asymptotically. Yet, closer observation shows that the classical power-law distribution is often…

统计理论 · 数学 2022-07-18 Swarup Chattopadhyay , Tanujit Chakraborty , Kuntal Ghosh , Asit K. das

Real-life networks often encounter vertex dysfunctions, which are usually followed by recoveries after appropriate maintenances. In this paper we present our research on a model of scale-free networks whose vertices are regularly removed…

物理与社会 · 物理学 2013-10-08 Linjun Li , Xiangwen Wang

We analyze a simple model for growing tree networks and find that although it never percolates, there is an anomalously large cluster at finite size. We study the growth of both the maximal cluster and the cluster containing the original…

统计力学 · 物理学 2007-05-23 David Lancaster

Co-evolution exhibited by a network system, involving the intricate interplay between the dynamics of the network itself and the subsystems connected by it, is a key concept for understanding the self-organized, flexible nature of…

物理与社会 · 物理学 2012-11-14 Takaaki Aoki , Toshio Aoyagi

In many real world networks, the number of links increases nonlinearly with the number of nodes. Models of such accelerated growth have been considered earlier with deterministic and stochastic number of links. Here we consider stochastic…

统计力学 · 物理学 2009-11-10 Parongama Sen

We extend the standard scale-free network model to include a ``triad formation step''. We analyze the geometric properties of networks generated by this algorithm both analytically and by numerical calculations, and find that our model…

无序系统与神经网络 · 物理学 2009-11-07 Petter Holme , Beom Jun Kim

We propose a deterministic weighted scale-free small-world model for considering pseudofractal web with the coevolution of topology and weight. In the model, we have the degree distribution exponent $\gamma$ restricted to a range between 2…

统计力学 · 物理学 2011-02-03 Yichao Zhang , Zhongzhi Zhang , Shuigeng Zhou , Jihong Guan

We study evolving networks where new nodes when attached to the network form links with other nodes of preferred distances. A particular case is where always the shortest distances are selected (``make friends with the friends of your…

统计力学 · 物理学 2009-11-07 J. Jost , M. P. Joy

We bring rigor to the vibrant activity of detecting power laws in empirical degree distributions in real-world networks. We first provide a rigorous definition of power-law distributions, equivalent to the definition of regularly varying…

物理与社会 · 物理学 2019-10-23 Ivan Voitalov , Pim van der Hoorn , Remco van der Hofstad , Dmitri Krioukov

We propose a model that generates a new class of networks exhibiting power-law degree distribution with a spectrum of exponents depending on the number of links ($m$) with which incoming nodes join the existing network. Unlike the…

物理与社会 · 物理学 2018-01-09 Kamrul Hassan , Liana Islam

Complex networks across various fields are often considered to be scale free -- a statistical property usually solely characterized by a power-law distribution of the nodes' degree $k$. However, this characterization is incomplete. In…

物理与社会 · 物理学 2023-10-24 Xiangyi Meng , Bin Zhou

We introduce the notion of globally updating evolution for a class of weighted networks, in which the weight of a link is characterized by the amount of data packet transport flowing through it. By noting that the packet transport over the…

统计力学 · 物理学 2009-11-10 K. -I. Goh , B. Kahng , D. Kim

We propose and study a model of traffic in communication networks. The underlying network has a structure that is tunable between a scale-free growing network with preferential attachments and a random growing network. To model realistic…

网络与互联网体系结构 · 计算机科学 2008-06-12 Zonghua Liua , Weichuan Ma , Huan Zhang , Yin Sun , P. M. Hui

We propose that negative degree correlation among nodes in a network of nonlinear oscillators, often detected in real world networks, is motivated by its positive effects on synchronizability. In so doing, we use a novel methodology to…

无序系统与神经网络 · 物理学 2007-12-08 Mario di Bernardo , Franco Garofalo , Francesco Sorrentino

We offer an example of an network model with a power law degree distribution, P(k) ~ k^{-alpha}, for nodes but which nevertheless has a well-defined geography and a nonzero threshold percolation probability for alpha>2, the range of…

统计力学 · 物理学 2009-11-07 C. P. Warren , L. M. Sander , I. M. Sokolov

We introduce a deterministic model for scale-free networks, whose degree distribution follows a power-law with the exponent $\gamma$. At each time step, each vertex generates its offsprings, whose number is proportional to the degree of…

统计力学 · 物理学 2009-11-07 S. Jung , S. Kim , B. Kahng