國立虎尾科技大學 |

Predicting the Dynamics of Research Impact

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Predicting the Dynamics of Research Impact/ edited by Yannis Manolopoulos, Thanasis Vergoulis.
其他作者:	Vergoulis, Thanasis.
面頁冊數:	XX, 290 p. 63 illus., 49 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Machine Learning. -
電子資源:	https://doi.org/10.1007/978-3-030-86668-6
ISBN:	9783030866686

Predicting the Dynamics of Research Impact
Predicting the Dynamics of Research Impact[electronic resource] /edited by Yannis Manolopoulos, Thanasis Vergoulis. - 1st ed. 2021. - XX, 290 p. 63 illus., 49 illus. in color.online resource.

1. On Complete Evaluation Systems -- 2. Extrinsic Factors Affecting Citation Frequencies of Research Articles -- 3. Remarks on Dynamics of Research Production of Researchers and Research Organizations -- 4. Does Publicity in the Science Press Drive Citations? A Vindication of Peer Review -- 5. Ranking Papers by Expected Short-Term Impact -- 6. Properties of an Indicator of Citation Durability of Research Articles -- 7. Wider, or Deeper! On Predicting Future of Scientific Articles by Influence Dispersion Tree -- 8. Can Author Collaboration Reveal Impact? The Case of h-index -- 9. Identification of Promising Researchers through Fast-and-frugal Heuristics -- 10. Scientific Impact Vitality: The Citation Currency Ratio and Citation Currency Exergy Indicators -- 11. Detection, Analysis, and Prediction of Research Topics with Scientific Knowledge Graphs -- 12. Early Detection of Emerging Technologies using Temporal Features -- 13. Link Prediction in Bibliographic Networks.

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 in the respective fields to students and developers being curious about the covered subjects.

ISBN: 9783030866686

Standard No.: 10.1007/978-3-030-86668-6doiSubjects--Topical Terms:

1137723
Machine Learning.

LC Class. No.: QA75.5-76.95

Dewey Class. No.: 025.04

Predicting the Dynamics of Research Impact
LDR:04438nam a22004095i 4500 001 1050551
003 DE-He213
005 20210922184833.0
007 cr nn 008mamaa
008 220103s2021 sz | s |||| 0|eng d
020 $a 9783030866686 $9 978-3-030-86668-6
024 7 $a 10.1007/978-3-030-86668-6 $2 doi
035 $a 978-3-030-86668-6
050 4 $a QA75.5-76.95
072 7 $a UNH $2 bicssc
072 7 $a COM030000 $2 bisacsh
072 7 $a UNH $2 thema
072 7 $a UND $2 thema
082 0 4 $a 025.04 $2 23
245 1 0 $a Predicting the Dynamics of Research Impact $h [electronic resource] / $c edited by Yannis Manolopoulos, Thanasis Vergoulis.
250 $a 1st ed. 2021.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a XX, 290 p. 63 illus., 49 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a 1. On Complete Evaluation Systems -- 2. Extrinsic Factors Affecting Citation Frequencies of Research Articles -- 3. Remarks on Dynamics of Research Production of Researchers and Research Organizations -- 4. Does Publicity in the Science Press Drive Citations? A Vindication of Peer Review -- 5. Ranking Papers by Expected Short-Term Impact -- 6. Properties of an Indicator of Citation Durability of Research Articles -- 7. Wider, or Deeper! On Predicting Future of Scientific Articles by Influence Dispersion Tree -- 8. Can Author Collaboration Reveal Impact? The Case of h-index -- 9. Identification of Promising Researchers through Fast-and-frugal Heuristics -- 10. Scientific Impact Vitality: The Citation Currency Ratio and Citation Currency Exergy Indicators -- 11. Detection, Analysis, and Prediction of Research Topics with Scientific Knowledge Graphs -- 12. Early Detection of Emerging Technologies using Temporal Features -- 13. Link Prediction in Bibliographic Networks.
520 $a 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 in the respective fields to students and developers being curious about the covered subjects.
650 2 4 $a Machine Learning. $3 1137723
650 2 4 $a Library Science. $3 670094
650 1 4 $a Information Storage and Retrieval. $3 593926
650 0 $a Machine learning. $3 561253
650 0 $a Library science. $3 561163
650 0 $a Information storage and retrieval. $3 1069252
700 1 $a Vergoulis, Thanasis. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1354893
700 1 $a Manolopoulos, Yannis. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 669825
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030866679
776 0 8 $i Printed edition: $z 9783030866693
776 0 8 $i Printed edition: $z 9783030866709
856 4 0 $u https://doi.org/10.1007/978-3-030-86668-6
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)