Diffusion of scientific credits and the ranking of scientists

By Filippo Radicchi, Santo Fortunato, Benjamin Markines, Alessandro Vespignani. Physical review. E, Statistical, nonlinear, and soft matter physics, 2009

300 citations recorded by the source corpus.

Recently, the abundance of digital data is enabling the implementation of graph-based ranking algorithms that provide system level analysis for ranking publications and authors. Here, we take advantage of the entire Physical Review publication archive (1893-2006) to construct authors' networks where weighted edges, as measured from opportunely normalized citation counts, define a proxy for the mechanism of scientific credit transfer. On this network, we define a ranking method based on a diffusion algorithm that mimics the spreading of scientific credits on the network. We compare the results obtained with our algorithm with those obtained by local measures such as the citation count and provide a statistical analysis of the assignment of major career awards in the area of physics. A website where the algorithm is made available to perform customized rank analysis can be found at the address http://www.physauthorsrank.org.

Subfields: Statistical and Nonlinear Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/8c4e43e83f802919166943cd428de0387b13959b

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Citations in the corpus

The Matthew effect in empirical data (2014, background): “From citation distributions [104–109], co-authorship networks [98] and the formation of research teams [110,111], to the ranking of researchers [112–114] and the predictability of their success [72,115–117]—how we do science has become a science in its own right.”
Reputation and impact in academic careers (2013, background): “[2] Radicchi F, Fortunato S, Markines B, Vespignani A (2009) Dif-”
The science of science: from the perspective of complex systems (2017, methodology): “An important one is the citation network between scientists [91].”
Quantifying Long-Term Scientific Impact (2013, background): “cy of use: citations (1–10). The reliance on citation based measures, from the Hirsch index (4) to the g-index (11), from impact factors (1) to eigenfactors (12), and on diverse ranking based metrics (13), lies in the (often debated) perception that citations offer a quantitative proxy of a discover”
Vital nodes identification in complex networks (2016, methodology): “To provide more precise measurements, some researchers borrow the idea of identifying vital nodes and then propose some new metrics by harnessing various relations among publications and authors, like quantifying the publications’ influences through the citation relations among publications [347, 34”
Identifying influential nodes in complex networks (2012, unknown): “but more time-consuming [12,13,22,23]).”