The book contains new models of bibliometric analysis based on 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.

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

Autorentext

Fuad Aleskerov is the Head of the International Centre of Decision Analysis and Choice, the Head of the Department of Mathematics for Economics at the National Research University Higher School of Economics in Moscow, Russia, the Head of the Laboratory of Choice Theory and Decision Analysis at the Institute of Control Sciences of the Russian Academy of Sciences, and a Member of Academia Europaea among other scientific societies. He is the author of 12 books and more than 200 articles. Olga Khutorskaya is a Senior Researcher at the Institute of Control Sciences of Russian Academy of Sciences. She is a specialist on medical cybernetics, an author of more than 60 articles and 2 patents in the field related to Parkinson’s disease and other neurodegenerative diseases. Anna Stepochkina is a Researcher at the International Centre of Decision Choice and Analysis at the Higher School of Economics in Moscow,Russia. She specializes in the analysis of network and bibliometric data, particularly in data preprocessing, centrality and stability analysis. Her publications focus on the network analysis of scientific research on Parkinson's disease. Viacheslav Yakuba is a Senior Researcher at the Institute of Control Sciences of the Russian Academy of Sciences and at the International Centre of Decision Choice and Analysis at the Higher School of Economics in Moscow, Russia. His current research interests focus on centrality analysis of networks as well as software implementation of the algorithms of newly developed centrality and stability indices in the networks. He has over 70 publications. Ksenia Zinovyeva is a Researcher at the International Centre of Decision Choice and Analysis at the Higher School of Economics in Moscow, Russia. Her work primarily focuses on network analysis, with a special emphasis in the field of bibliometric analysis. She has published several articles exploring citation patterns in publications related to Parkinson's disease research.

Zusammenfassung

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.

Inhalt

Chapter 1. A survey of the network bibliometric analysis.- Chapter 2. Data description and network analysis models.- Chapter 3. An analysis of publication citations.- Chapter 4. An analysis of terms.- Chapter 5. An analysis of journals citations.- Chapter 6. An analysis of citations of publication authors and their affiliations.