Modeling the coevolution between citations and coauthorships in scientific papers
arXiv:1607.04884 · doi:10.1007/s11192-017-2359-1
Abstract
Collaborations and citations within scientific research grow simultaneously and interact dynamically. Modelling the coevolution between them helps to study many phenomena that can be approached only through combining citation and coauthorship data. A geometric graph for the coevolution is proposed, the mechanism of which synthetically expresses the interactive impacts of authors and papers in a geometrical way. The model is validated against a data set of papers published on PNAS during 2007-2015. The validation shows the ability to reproduce a range of features observed with citation and coauthorship data combined and separately. Particularly, in the empirical distribution of citations per author there exist two limits, in which the distribution appears as a generalized Poisson and a power-law respectively. Our model successfully reproduces the shape of the distribution, and provides an explanation for how the shape emerges via the decisions of authors. The model also captures the empirically positive correlations between the numbers of authors' papers, citations and collaborators.
References in corpus (11)
- The Matthew effect in empirical data
- Diffusion of scientific credits and the ranking of scientists
- Principles of scientific research team formation and evolution
- Network Cosmology
- Modes of Collaboration in Modern Science - Beyond Power Laws and Preferential Attachment
- Growth and structure of Slovenia's scientific collaboration network
- Distortive Effects of Initial-Based Name Disambiguation on Measurements of Large-Scale Coauthorship Networks
- Inheritance patterns in citation networks reveal scientific memes
- Modelling Citation Networks
- Zipf's law and log-normal distributions in measures of scientific output across fields and institutions: 40 years of Slovenia's research as an example
- Scale-invariant geometric random graphs