25 research outputs found
Nonuniversal power law scaling in the probability distribution of scientific citations
We develop a model for the distribution of scientific citations. The model
involves a dual mechanism: in the direct mechanism, the author of a new paper
finds an old paper A and cites it. In the indirect mechanism, the author of a
new paper finds an old paper A only via the reference list of a newer
intermediary paper B, which has previously cited A. By comparison to citation
databases, we find that papers having few citations are cited mainly by the
direct mechanism. Papers already having many citations ('classics') are cited
mainly by the indirect mechanism. The indirect mechanism gives a power-law
tail. The 'tipping point' at which a paper becomes a classic is about 21
citations for papers published in the Institute for Scientific Information
(ISI) Web of Science database in 1981, 29 for Physical Review D papers
published from 1975-1994, and 39 for all publications from a list of high
h-index chemists assembled in 2007. The power-law exponent is not universal.
Individuals who are highly cited have a systematically smaller exponent than
individuals who are less cited.Comment: 7 pages, 3 figures, 2 table
Quantifying Long-Term Scientific Impact
The lack of predictability of citation-based measures frequently used to
gauge impact, from impact factors to short-term citations, raises a fundamental
question: Is there long-term predictability in citation patterns? Here, we
derive a mechanistic model for the citation dynamics of individual papers,
allowing us to collapse the citation histories of papers from different
journals and disciplines into a single curve, indicating that all papers tend
to follow the same universal temporal pattern. The observed patterns not only
help us uncover basic mechanisms that govern scientific impact but also offer
reliable measures of influence that may have potential policy implications
Uncovering the dynamics of citations of scientific papers
We demonstrate a comprehensive framework that accounts for citation dynamics
of scientific papers and for the age distribution of references. We show that
citation dynamics of scientific papers is nonlinear and this nonlinearity has
far-reaching consequences, such as diverging citation distributions and runaway
papers. We propose a nonlinear stochastic dynamic model of citation dynamics
based on link copying/redirection mechanism. The model is fully calibrated by
empirical data and does not contain free parameters. This model can be a basis
for quantitative probabilistic prediction of citation dynamics of individual
papers and of the journal impact factor.Comment: 18 pages, 7 figure
Statistical regularities in the rank-citation profile of scientists
Recent science of science research shows that scientific impact measures for journals and individual articles have quantifiable regularities across both time and discipline. However, little is known about the scientific impact distribution at the scale of an individual scientist. We analyze the aggregate production and impact using the rank-citation profile ci(r) of 200 distinguished professors and 100 assistant professors. For the entire range of paper rank r, we fit each ci(r) to a common distribution function. Since two scientists with equivalent Hirsch h-index can have significantly different ci(r) profiles, our results demonstrate the utility of the βi scaling parameter in conjunction with hi for quantifying individual publication impact. We show that the total number of citations Ci tallied from a scientist's Ni papers scales as . Such statistical regularities in the input-output patterns of scientists can be used as benchmarks for theoretical models of career progress