Is there quantifiable regularity and predictability in citation patterns? It is clear that papers that have been cited frequently tend to accumulate more citations. It is also clear that, with time, even the most novel paper loses its currency. Some papers, however, seem to have an inherent ” fitness” that can be interpreted as a community's response to the research. Wang et al. (p. 127; see the Perspective by Evans) developed a mechanistic model to predict citation history. The model links a paper's ultimate impact, represented by the total number of citations the paper will ever receive, to a single measurable parameter inferred from its early citation history. The model was used to identify factors that influence a journal's impact factor.
%0 Journal Article
%1 Wang2013Quantifying
%A Wang, Dashun
%A Song, Chaoming
%A Barabási, Albert-László
%D 2013
%I American Association for the Advancement of Science
%J Science
%K scientific\_networks, success predictive-models
%N 6154
%P 127--132
%R 10.1126/science.1237825
%T Quantifying Long-Term Scientific Impact
%U http://dx.doi.org/10.1126/science.1237825
%V 342
%X Is there quantifiable regularity and predictability in citation patterns? It is clear that papers that have been cited frequently tend to accumulate more citations. It is also clear that, with time, even the most novel paper loses its currency. Some papers, however, seem to have an inherent ” fitness” that can be interpreted as a community's response to the research. Wang et al. (p. 127; see the Perspective by Evans) developed a mechanistic model to predict citation history. The model links a paper's ultimate impact, represented by the total number of citations the paper will ever receive, to a single measurable parameter inferred from its early citation history. The model was used to identify factors that influence a journal's impact factor.
@article{Wang2013Quantifying,
abstract = {{Is there quantifiable regularity and predictability in citation patterns? It is clear that papers that have been cited frequently tend to accumulate more citations. It is also clear that, with time, even the most novel paper loses its currency. Some papers, however, seem to have an inherent ” fitness” that can be interpreted as a community's response to the research. Wang et al. (p. 127; see the Perspective by Evans) developed a mechanistic model to predict citation history. The model links a paper's ultimate impact, represented by the total number of citations the paper will ever receive, to a single measurable parameter inferred from its early citation history. The model was used to identify factors that influence a journal's impact factor.}},
added-at = {2019-06-10T14:53:09.000+0200},
author = {Wang, Dashun and Song, Chaoming and Barab\'{a}si, Albert-L\'{a}szl\'{o}},
biburl = {https://www.bibsonomy.org/bibtex/279325bb7dc021cd6a918d45cf9b9fda3/nonancourt},
citeulike-article-id = {12690387},
citeulike-linkout-0 = {http://dx.doi.org/10.1126/science.1237825},
citeulike-linkout-1 = {http://www.sciencemag.org/content/342/6154/127.abstract},
citeulike-linkout-2 = {http://www.sciencemag.org/content/342/6154/127.full.pdf},
citeulike-linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/24092745},
citeulike-linkout-4 = {http://www.hubmed.org/display.cgi?uids=24092745},
day = 04,
doi = {10.1126/science.1237825},
interhash = {a60be529249fffe400cb3a1ba1ce2e85},
intrahash = {79325bb7dc021cd6a918d45cf9b9fda3},
issn = {1095-9203},
journal = {Science},
keywords = {scientific\_networks, success predictive-models},
month = oct,
number = 6154,
pages = {127--132},
pmid = {24092745},
posted-at = {2013-10-04 14:24:04},
priority = {2},
publisher = {American Association for the Advancement of Science},
timestamp = {2019-07-31T12:33:48.000+0200},
title = {{Quantifying Long-Term Scientific Impact}},
url = {http://dx.doi.org/10.1126/science.1237825},
volume = 342,
year = 2013
}