株式会社極東書店トップ商品一覧Predicting the Dynamics of Research Impact. 2021 ed.

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Predicting the Dynamics of Research Impact. 2021 ed.

Predicting the Dynamics of Research Impact. 2021 ed.

・ISBN 978-3-030-86667-9 hard EUR 169.99

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お気に入り
著者・編者Manolopoulos, Yannis / Vergoulis, Thanasis (eds.),
出版社 (Springer Nature Switzerland AG, SZ)
出版年月2021
ページ数290 pp.
言語ENG
ニュース番号<A02-16562>

解説

This book provides its readers with an introduction to interesting prediction and science dynamics problems in the field of Science of Science. Prediction focuses on the forecasting of future performance (or impact) of an entity, either a research article or a scientist, and also the prediction of future links in collaboration networks or identifying missing links in citation networks.

The single chapters are written in a way that help the reader gain a detailed technical understanding of the corresponding subjects, the strength and weaknesses of the state-of-the-art approaches for each described problem, and the currently open challenges. While chapter 1 provides a useful contribution in the theoretical foundations of the fields of scientometrics and science of science, chapters 2-4 turn the focal point to the study of factors that affect research impact and its dynamics. Chapters 5-7 then focus on article-level measures that quantify the current and future impact of scientific articles. Next, chapters 8-10 investigate subjects relevant to predicting the future impact of individual researchers. Finally, chapters 11-13 focus on science evolution and dynamics, leveraging heterogeneous and interconnected data, where the analysis of research topic trends and their evolution has always played a key role in impact prediction approaches and quantitative analyses in the field of bibliometrics. Each chapter can be read independently, since it includes a detailed description of the problem being investigated along with a thorough discussion and study of the respective state-of-the-art.

Due to the cross-disciplinary character of the Science of Science field, the book may be useful to interested readers from a variety of disciplines like information science, information retrieval, network science, informetrics, scientometrics, and machine learning, to name a few. The profiles of the readers may also be diverse ranging from researchers and professors inthe respective fields to students and developers being curious about the covered subjects.