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Record Nr. |
UNINA9911049099603321 |
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Autore |
Aleskerov F. T (Faud Tagi ogly) |
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Titolo |
Bibliometric Analysis by Network Models : Identifying Trends in Scientific Literature / / by Fuad Aleskerov, Olga Khutorskaya, Anna Stepochkina, Vyacheslav Yakuba, Ksenia Zinovyeva |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2026 |
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ISBN |
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Edizione |
[1st ed. 2026.] |
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Descrizione fisica |
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1 online resource (364 pages) |
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Collana |
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Contemporary Systems Thinking |
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Disciplina |
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Soggetti |
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Sampling (Statistics) |
Biometry |
Neurosciences |
Methodology of Data Collection and Processing |
Biostatistics |
Neuroscience |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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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. |
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Sommario/riassunto |
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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 |
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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. |
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