000 | 03502nam a22005175i 4500 | ||
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001 | 978-3-030-28169-4 | ||
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007 | cr nn 008mamaa | ||
008 | 190926s2019 gw | s |||| 0|eng d | ||
020 |
_a9783030281694 _9978-3-030-28169-4 |
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024 | 7 |
_a10.1007/978-3-030-28169-4 _2doi |
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_a621 _223 |
100 | 1 |
_aGolosovsky, Michael. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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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. |
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300 |
_aXIV, 121 p. 53 illus., 52 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aSpringerBriefs in Complexity, _x2191-5326 |
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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 |
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