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Citation analysis and dynamics of ci...

Golosovsky, Michael.

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Citation analysis and dynamics of citation networks

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Citation analysis and dynamics of citation networks/ by Michael Golosovsky.
作者:	Golosovsky, Michael.
出版者:	Cham :Springer International Publishing : : 2019.,
面頁冊數:	xiv, 121 p. :ill. (some col.), digital ;24 cm.
內容註:	Chapter1: Introduction -- Chapter2: Complex network of scientific papers -- Chapter3: Stochastic modeling of references and citations -- Chapter4: Citation dynamics of individual papers -model calibration -- Chapter5: Model validation -- Chapter6: Comparison of citation dynamics for different disciplines -- Chapter7: Prediction of citation dynamics of individual papers -- Chapter8: Power-law citation distributions are not scale-free -- Chapter9: Comparison to existing models.
Contained By:	Springer eBooks
標題:	Scientific literature - Statistical methods. -
電子資源:	https://doi.org/10.1007/978-3-030-28169-4
ISBN:	9783030281694

Citation analysis and dynamics of citation networks
Golosovsky, Michael.

Citation analysis and dynamics of citation networks[electronic resource] /by Michael Golosovsky. - Cham :Springer International Publishing :2019. - xiv, 121 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs in complexity,2191-5326. - SpringerBriefs in complexity..

Chapter1: Introduction -- Chapter2: Complex network of scientific papers -- Chapter3: Stochastic modeling of references and citations -- Chapter4: Citation dynamics of individual papers -model calibration -- Chapter5: Model validation -- Chapter6: Comparison of citation dynamics for different disciplines -- Chapter7: Prediction of citation dynamics of individual papers -- Chapter8: Power-law citation distributions are not scale-free -- Chapter9: Comparison to existing models.

This book deals with the science of science by applying network science methods to citation networks and uniquely presents a physics-inspired model of citation dynamics. This stochastic model of citation dynamics is based on a well-known copying or recursive search mechanism. The measurements covered in this text yield parameters of the model and reveal that citation dynamics of scientific papers is not linear, as was previously assumed. This nonlinearity has far-reaching consequences including non-stationary citation distributions, diverging citation trajectories of similar papers, and runaways or "immortal papers" with an infinite citation lifespan. The author shows us that nonlinear stochastic models of citation dynamics can be the basis for a quantitative probabilistic prediction of citation dynamics of individual papers and of the overall journal impact factor. This book appeals to students and researchers from differing subject areas working in network science and bibliometrics.

ISBN: 9783030281694

Standard No.: 10.1007/978-3-030-28169-4doiSubjects--Topical Terms:

3414490
Scientific literature
--Statistical methods.

LC Class. No.: Q225.5 / .G656 2019

Dewey Class. No.: 507

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505 0 $a Chapter1: Introduction -- Chapter2: Complex network of scientific papers -- Chapter3: Stochastic modeling of references and citations -- Chapter4: Citation dynamics of individual papers -model calibration -- Chapter5: Model validation -- Chapter6: Comparison of citation dynamics for different disciplines -- Chapter7: Prediction of citation dynamics of individual papers -- Chapter8: Power-law citation distributions are not scale-free -- Chapter9: Comparison to existing models.
520 $a This book deals with the science of science by applying network science methods to citation networks and uniquely presents a physics-inspired model of citation dynamics. This stochastic model of citation dynamics is based on a well-known copying or recursive search mechanism. The measurements covered in this text yield parameters of the model and reveal that citation dynamics of scientific papers is not linear, as was previously assumed. This nonlinearity has far-reaching consequences including non-stationary citation distributions, diverging citation trajectories of similar papers, and runaways or "immortal papers" with an infinite citation lifespan. The author shows us that nonlinear stochastic models of citation dynamics can be the basis for a quantitative probabilistic prediction of citation dynamics of individual papers and of the overall journal impact factor. This book appeals to students and researchers from differing subject areas working in network science and bibliometrics.
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