Поздравляем коллег с выходом новой книги Bibliometric Analysis by Network Models: Identifying Trends in Scientific Literature!

Книга выпущена издательством Springer в 2026 году.

About this book

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.

Overview
Authors: Fuad Aleskerov, Olga Khutorskaya, Anna Stepochkina, Vyacheslav Yakuba, Ksenia Zinovyeva
* Provides a strong basis for extended publication analysis of different scientific fields
* Contains several new models to analyse different aspects of publications, in particular, new network models
* Discusses centrality analysis of article citation networks, journal citation networks, and methods of semantic analysis

Более подробную информацию можно получить по ссылке Bibliometric Analysis by Network Models: Identifying Trends in Scientific Literature | Springer Nature Link (formerly SpringerLink)