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Citation Analysis and Dynamics of Citation Networks [electronic resource] / by Michael Golosovsky.

By: Golosovsky, Michael [author.]Material type: Text

TextSeries: SpringerBriefs in ComplexityPublisher: Cham : Springer International Publishing : Imprint: Springer, 2019Edition: 1st ed. 2019Description: XIV, 121 p. 53 illus., 52 illus. in color. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783030281694Subject(s): Sociophysics | Econophysics | System theory | Big data | Data-driven Science, Modeling and Theory Building | Complex Systems | Big Data | Big Data/AnalyticsDDC classification: 621 LOC classification: QC1-999Online resources: Click here to access online

Contents:

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.

Summary: 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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Holdings
Item type	Current library	Call number	Status	Date due	Barcode	Item holds
e-Books	Central Library, Sikkim University	621 (Browse shelf(Opens below))	Not for loan		E-3131

Total holds: 0

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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