Bibliometric Analysis by Network Models Identifying Trends in Scientific Literature (True EPUB)
Bibliometric Analysis by Network Models Identifying Trends in Scientific Literature (True EPUB) | 6.59 MB
Title: Bibliometric Analysis by Network Models
Author: Fuad Aleskerov, Olga Khutorskaya, Anna Stepochkina, Vyacheslav Yakuba, Ksenia Zinovyeva
Category: Nonfiction, Health & Well Being, Medical, Reference, Biostatistics, Science & Nature, Mathematics, Statistics, Science, Biological Sciences
Language: English | 222 Pages | ISBN: 9783032091710
Description:
The book contains new models of bibliometric analysis based on new centrality measures in network analysis, pattern analysis and stability analysis. A distinctive feature of these centrality measures is that they account for the parameters of vertices and group influence of vertices to a vertex. This reveals specific groups of publications, authors, terms, journals and affiliations of the authors depending on different parameters of publications. Pattern analysis and stability analysis allow the tendencies in developing of the field of research over years to be revealed. These new models are illustrated by an analysis of 39,811 articles on various aspects of Parkinson's disease, published between 2015 and 2021. This methodology can be useful for researchers of any scientific domain, because it enables them to identify key and actively developing trends as well as major players in the field. Moreover, this approach allows to determine main tendencies in the entire research community as well as in the specific parts of it that may have gone unnoticed before. The obtained results of the analysis are useful not only for researchers but also for journals, editorial teams, scientific organizations, and investors.
DOWNLOAD:
https://rapidgator.net/file/0f35358aeb088e79a350cdef9c37064b/Bibliometric_Analysis_by_Network_Models_Identifying_Trends_in_Scientific_Literature_True_EPUB.epub
https://nitroflare.com/view/C038D0E6EE82588/Bibliometric_Analysis_by_Network_Models_Identifying_Trends_in_Scientific_Literature_True_EPUB.epub
The book contains new models of bibliometric analysis based on new centrality measures in network analysis, pattern analysis and stability analysis. A distinctive feature of these centrality measures is that they account for the parameters of vertices and group influence of vertices to a vertex. This reveals specific groups of publications, authors, terms, journals and affiliations of the authors depending on different parameters of publications. Pattern analysis and stability analysis allow the tendencies in developing of the field of research over years to be revealed. These new models are illustrated by an analysis of 39,811 articles on various aspects of Parkinson's disease, published between 2015 and 2021. This methodology can be useful for researchers of any scientific domain, because it enables them to identify key and actively developing trends as well as major players in the field. Moreover, this approach allows to determine main tendencies in the entire research community as well as in the specific parts of it that may have gone unnoticed before. The obtained results of the analysis are useful not only for researchers but also for journals, editorial teams, scientific organizations, and investors.
DOWNLOAD:
https://rapidgator.net/file/0f35358aeb088e79a350cdef9c37064b/Bibliometric_Analysis_by_Network_Models_Identifying_Trends_in_Scientific_Literature_True_EPUB.epub
https://nitroflare.com/view/C038D0E6EE82588/Bibliometric_Analysis_by_Network_Models_Identifying_Trends_in_Scientific_Literature_True_EPUB.epub
