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In this work we make an attempt to understand social networks from a mathematical viewpoint. In the first instance we consider a network where each node representing an individual can connect with a neighbouring node with a certain…

物理与社会 · 物理学 2022-08-17 Vaibhav Wasnik

Despite their diverse origin, networks of large real-world systems reveal a number of common properties including small-world phenomena, scale-free degree distributions and modularity. Recently, network self-similarity as a natural outcome…

适应与自组织系统 · 物理学 2012-02-09 Neli Blagus , Lovro Šubelj , Marko Bajec

Social networks are organized into communities with dense internal connections, giving rise to high values of the clustering coefficient. In addition, these networks have been observed to be assortative, i.e. highly connected vertices tend…

物理与社会 · 物理学 2016-09-08 R. Toivonen , J. -P. Onnela , J. Saramäki , J. Hyvönen , K. Kaski

In this article, we propose a growing network model based on an optimal policy involving both topological and geographical measures. In this model, at each time step, a new node, having randomly assigned coordinates in a $1 \times 1$…

物理与社会 · 物理学 2007-05-23 Yan-Bo Xie , Tao Zhou , Wen-Jie Bai , Guanrong Chen , Wei-Ke Xiao , Bing-Hong Wang

In this letter, we investigate the detailed epidemic spreading process in scale-free networks with links' weights that denote familiarity between two individuals and find that spreading velocity reaches a peak quickly then decays in a…

统计力学 · 物理学 2009-11-10 Gang Yan , Tao Zhou , Jie Wang , Zhong-Qian Fu , Bing-Hong Wang

This paper develops strategic foundations for an important statistical model of random networks with heterogeneous expected degrees. Based on this, we show how social networking services that subtly alter the costs and indirect benefits of…

应用统计 · 统计学 2010-04-09 Benjamin Golub , Yair Livne

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

Large-scale human social network structure is typically inferred from digital trace samples of online social media platforms or mobile communication data. Instead, here we investigate the social network structure of a complete population,…

物理与社会 · 物理学 2022-11-29 Eszter Bokányi , Eelke M. Heemskerk , Frank W. Takes

Recently several authors have proposed stochastic evolutionary models for the growth of the web graph and other networks that give rise to power-law distributions. These models are based on the notion of preferential attachment leading to…

软凝聚态物质 · 物理学 2007-05-23 Trevor Fenner , Mark Levene , George Loizou

Critical states are sometimes identified experimentally through power-law statistics or universal scaling functions. We show here that such features naturally emerge from networks in self-sustained irregular regimes away from criticality.…

神经元与认知 · 定量生物学 2017-02-08 Jonathan Touboul , Alain Destexhe

We found that models of evolving random networks exhibit dynamic scaling similar to scaling of growing surfaces. It is demonstrated by numerical simulations of two variants of the model in which nodes are added as well as removed [Phys.…

统计力学 · 物理学 2009-11-07 Miroslav Kotrla , Frantisek Slanina , Jakub Steiner

In the context of growing networks, we introduce a simple dynamical model that unifies the generic features of real networks: scale-free distribution of degree and the small world effect. While the average shortest path length increases…

凝聚态物理 · 物理学 2009-11-07 Konstantin Klemm , Victor M. Eguiluz

The probability distribution of number of ties of an individual in a social network follows a scale-free power-law. However, how this distribution arises has not been conclusively demonstrated in direct analyses of people's actions in…

We formulate a general model for the growth of scale-free networks under filtering information conditions--that is, when the nodes can process information about only a subset of the existing nodes in the network. We find that the…

统计力学 · 物理学 2009-11-07 Stefano Mossa , Marc Barthelemy , H. Eugene Stanley , Luis A. Nunes Amaral

Many real networks are equipped with short diameters, high clustering, and power-law degree distributions. With preferential attachment and network growth, the model by Barabasi and Albert simultaneously reproduces these properties, and…

无序系统与神经网络 · 物理学 2007-05-23 Naoki Masuda , Hiroyoshi Miwa , Norio Konno

The scale-free (SF) structure that commonly appears in many complex networks is one of the hot topics related to social, biological, and information sciences. The self-organized generation mechanisms are expected to be useful for efficient…

物理与社会 · 物理学 2007-05-23 Yukio Hayashi

A projective network model is a model that enables predictions to be made based on a subsample of the network data, with the predictions remaining unchanged if a larger sample is taken into consideration. An exchangeable model is a model…

物理与社会 · 物理学 2018-04-13 A. P. Kartun-Giles , D. Krioukov , J. P. Gleeson , Y. Moreno , G. Bianconi

We review the main tools which allow for the statistical characterization of weighted networks. We then present two case studies, the airline connection network and the scientific collaboration network, which are representative of critical…

统计力学 · 物理学 2009-11-10 Marc Barthelemy , Alain Barrat , Romualdo Pastor-Satorras , Alessandro Vespignani

Scale-free networks, in which the distribution of the degrees obeys a power-law, are ubiquitous in the study of complex systems. One basic network property that relates to the structure of the links found is the degree assortativity, which…

物理与社会 · 物理学 2015-06-22 Oliver Williams , Charo I. Del Genio

Scale-free (SF) networks and small world networks have been found to occur in very diverse contexts. It is this striking universality which makes one look for widely applicable mechanisms which lead to the formation of such networks. In…

无序系统与神经网络 · 物理学 2007-05-23 Markus Brede , John Finnigan