scholarly journals Optimal factor taxation in a scale free model of vertical innovation

2021 ◽  
Author(s):  
Barbara Annicchiarico ◽  
Valentina Antonaroli ◽  
Alessandra Pelloni
Keyword(s):  
Scale Free ◽  
Free Model ◽  
Vertical Innovation

scholarly journals Optimal Factor Taxation in a Scale Free Model of Vertical Innovation

2020 ◽  
Author(s):  
Barbara Annicchiarico ◽  
Valentina Antonaroli ◽  
Alessandra Pelloni
Keyword(s):  
Scale Free ◽  
Free Model ◽  
Vertical Innovation

Scale-Free Networks Based on the Value of Interest

2013 ◽  
Vol 753-755 ◽  
pp. 2959-2962
Author(s):  
Jun Tao Yang ◽  
Hui Wen Deng
Keyword(s):  
Network Model ◽  
Power Law ◽  
Scale Free Network ◽  
Power Law Distribution ◽  
Scale Free ◽  
Distribution Property ◽  
Scale Free Networks ◽  
Free Model ◽  
Free Network ◽  
Interest Model

Assigning the value of interest to each node in the network, we give a scale-free network model. The value of interest is related to the fitness and the degree of the node. Experimental results show that the interest model not only has the characteristics of the BA scale-free model but also has the characteristics of fitness model, and the network has a power-law distribution property.

Topology of whole-brain functional MRI networks: Improving the truncated scale-free model

2014 ◽  
Vol 405 ◽  
pp. 151-158 ◽  
Author(s):  
E. Ruiz Vargas ◽  
D.G.V. Mitchell ◽  
S.G. Greening ◽  
L.M. Wahl
Keyword(s):  
Functional Mri ◽  
Whole Brain ◽  
Scale Free ◽  
Free Model

scholarly journals Betweenness centrality of fractal and nonfractal scale-free model networks and tests on real networks

2007 ◽  
Vol 75 (5) ◽  
Author(s):  
Maksim Kitsak ◽  
Shlomo Havlin ◽  
Gerald Paul ◽  
Massimo Riccaboni ◽  
Fabio Pammolli ◽  
...  
Keyword(s):  
Betweenness Centrality ◽  
Scale Free ◽  
Free Model ◽  
Model Networks

scholarly journals Redes Colaborativas nos Estudos Métricos de Ciência e Tecnologia │ Collaborative Networks in Metric Studies of Science and Technology

2013 ◽  
Vol 9 (1) ◽  
Author(s):  
Samile Andréa de Souza Vanz
Keyword(s):  
Network Theory ◽  
Scientific Collaboration ◽  
Preferential Attachment ◽  
Collaborative Networks ◽  
Centrality Measures ◽  
Cluster Property ◽  
Scale Free ◽  
Free Model ◽  
History Of ◽  
The Subject

Resumo A teoria de redes passou a ser muito utilizada pela Bibliometria e Cientometria porque auxilia na interpretação e no entendimento dos dados resultantes das pesquisas realizadas na área. O artigo aborda um breve histórico das redes comentando desde o modelo aleatório de Erdós e Rényi aos modelos mais atuais. Apresenta as medidas mais importantes para redes de coautoria, como densidade e medidas de centralidade. Descreve pesquisas empíricas aplicadas em redes de coautoria e suas descobertas, como a propriedade de conexão preferencial, o nível de agrupamento e o modelo sem escala. Conclui que o entendimento da teoria de redes é fundamental para o estudo do fenômeno da coautoria e que os pesquisadores interessados na temática devem ampliar o uso da mesma em suas pesquisas.Palavras-chave Redes de coautoria, colaboração científica. Abstract The network theory became widely used for Bibliometrics and Scientometrics because it helps in the understanding and interpretation of data resulting from these studies. This article covers a brief history of networks and comments from the random model of Erdós and Rényi to most current models. It presents the most important measures for co-author networks, such as density and centrality measures. It also describes applied empirical research on networks of co-authorship and its findings, as the preferential attachment, cluster property and scale free model. The article concludes that the understanding of the network theory is crucial to the study of the phenomenon of co-authorship and that researchers interested in the subject should expand the use of this theory in their research.Keywords Co-author network, scientific collaboration.

SMALL WORLD AND SCALE FREE MODEL OF TRANSMISSION OF SARS

2005 ◽  
Vol 15 (05) ◽  
pp. 1745-1755 ◽  
Author(s):  
MICHAEL SMALL ◽  
CHI K. TSE
Keyword(s):  
Hong Kong ◽  
Severe Acute Respiratory Syndrome ◽  
Infection Control ◽  
Random Number ◽  
Small World ◽  
Small World Network ◽  
Computational Simulations ◽  
Scale Free ◽  
Free Model ◽  
Analytic Expression

We model transmission of the Severe Acute Respiratory Syndrome (SARS) associated coronavirus (SARS-CoV) in Hong Kong with a complex small world network. Each node in the network is connected to its immediate neighbors and a random number of geographically isolated nodes. Transmission can only occur along these links. We find that this model exhibits dynamics very similar to those observed during the SARS outbreak in 2003. We derive an analytic expression for the rate of infection and confirm this expression with computational simulations. An immediate consequence of this quantity is that the severity of the SARS epidemic in Hong Kong in 2003 was due to ineffectual infection control in hospitals (i.e. nosocomial transmission). If all infectious individuals were isolated as rapidly as they were identified the severity of the outbreak would have been minimal.

scholarly journals Navigation in small-world networks: a scale-free continuum model

2006 ◽  
Vol 43 (04) ◽  
pp. 1173-1180 ◽  
Author(s):  
Massimo Franceschetti ◽  
Ronald Meester
Keyword(s):  
Social Sciences ◽  
Scale Invariance ◽  
Small World ◽  
Local Information ◽  
Engineering Systems ◽  
Small World Networks ◽  
Social Contacts ◽  
Scale Free ◽  
Free Model ◽  
Random Connection Model

The small-world phenomenon, the principle that we are all linked by a short chain of intermediate acquaintances, has been investigated in mathematics and social sciences. It has been shown to be pervasive both in nature and in engineering systems like the World Wide Web. Work of Jon Kleinberg has shown that people, using only local information, are very effective at finding short paths in a network of social contacts. In this paper we argue that the underlying key to finding short paths is scale invariance. In order to appreciate scale invariance we suggest a continuum setting, since true scale invariance happens at all scales, something which cannot be observed in a discrete model. We introduce a random-connection model that is related to continuum percolation, and we prove the existence of a unique scale-free model, among a large class of models, that allows the construction, with high probability, of short paths between pairs of points separated by any distance.

Sign in / Sign up

Export Citation Format

Share Document