Citation analysis and dynamics of citation networks
- 作者： Golosovsky, Michael, author.
- 其他作者：
- 其他題名：
- SpringerBriefs in complexity.
- 出版： Cham : Springer International Publishing :Imprint: Springer
- 叢書名： SpringerBriefs in complexity,
- 主題： Scientific literature--Statistical methods. , Bibliographical citations. , Data-driven Science, Modeling and Theory Building. , Complex Systems. , Big Data. , Big Data/Analytics.
- ISBN： 9783030281694 (electronic bk.) 、 9783030281687 (paper)
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FIND@SFXID:
CGU
- 資料類型: 電子書
- 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.
- 摘要註: 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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讀者標籤：
- 系統號： 005463556 | 機讀編目格式
館藏資訊
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.