Scale-free analysis of scientific collaboration hyper- networks

Author(s):  
Chang Xiao ◽  
Zhou Lina ◽  
Hu Feng
2013 ◽  
Vol 9 (1) ◽  
Author(s):  
Samile Andréa de Souza Vanz

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.


2009 ◽  
Vol 12 (01) ◽  
pp. 113-129 ◽  
Author(s):  
A. P. MASUCCI ◽  
G. J. RODGERS

In this paper we deal with the structural properties of weighted networks. Starting from an empirical analysis of a linguistic network, we analyze the differences between the statistical properties of a real and a shuffled network. We show that the scale-free degree distribution and the scale-free weight distribution are induced by the scale-free strength distribution, that is Zipf's law. We test the result on a scientific collaboration network, that is a social network, and we define a measure – the vertex selectivity – that can distinguish a real network from a shuffled network. We prove, via an ad hoc stochastic growing network with second order correlations, that this measure can effectively capture the correlations within the topology of the network.


Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 502
Author(s):  
Hohyun Jung ◽  
Frederick Kin Hing Phoa

The degree distribution has attracted considerable attention from network scientists in the last few decades to have knowledge of the topological structure of networks. It is widely acknowledged that many real networks have power-law degree distributions. However, the deviation from such a behavior often appears when the range of degrees is small. Even worse, the conventional employment of the continuous power-law distribution usually causes an inaccurate inference as the degree should be discrete-valued. To remedy these obstacles, we propose a finite mixture model of truncated zeta distributions for a broad range of degrees that disobeys a power-law behavior in the range of small degrees while maintaining the scale-free behavior. The maximum likelihood algorithm alongside the model selection method is presented to estimate model parameters and the number of mixture components. The validity of the suggested algorithm is evidenced by Monte Carlo simulations. We apply our method to five disciplines of scientific collaboration networks with remarkable interpretations. The proposed model outperforms the other alternatives in terms of the goodness-of-fit.


2020 ◽  
Vol 43 ◽  
Author(s):  
Chris Fields ◽  
James F. Glazebrook

Abstract Gilead et al. propose an ontology of abstract representations based on folk-psychological conceptions of cognitive architecture. There is, however, no evidence that the experience of cognition reveals the architecture of cognition. Scale-free architectural models propose that cognition has the same computational architecture from sub-cellular to whole-organism scales. This scale-free architecture supports representations with diverse functions and levels of abstraction.


2010 ◽  
Author(s):  
Takuma Takehara ◽  
Tumio Ochiai ◽  
Kosuke Tamiguchi ◽  
Naoto Suzuki
Keyword(s):  

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