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020 _a9783030281694
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024 7 _a10.1007/978-3-030-28169-4
_2doi
040 _cCUS
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082 0 4 _a621
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100 1 _aGolosovsky, Michael.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aCitation Analysis and Dynamics of Citation Networks
_h[electronic resource] /
_cby Michael Golosovsky.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aXIV, 121 p. 53 illus., 52 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Complexity,
_x2191-5326
505 0 _aChapter1: 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 _aThis 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.
650 0 _aSociophysics.
650 0 _aEconophysics.
650 0 _aSystem theory.
650 0 _aBig data.
650 1 4 _aData-driven Science, Modeling and Theory Building.
_0https://scigraph.springernature.com/ontologies/product-market-codes/P33030
650 2 4 _aComplex Systems.
_0https://scigraph.springernature.com/ontologies/product-market-codes/M13090
650 2 4 _aBig Data.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I29120
650 2 4 _aBig Data/Analytics.
_0https://scigraph.springernature.com/ontologies/product-market-codes/522070
830 0 _aSpringerBriefs in Complexity,
_x2191-5326
856 4 0 _uhttps://doi.org/10.1007/978-3-030-28169-4
912 _aZDB-2-PHA
912 _aZDB-2-SXP
942 _cEBK
999 _c207688
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