The simultaneous evolution of author and paper networks

By Katy Börner, Jeegar T Maru, Robert L. Goldstone. Proceedings of the National Academy of Sciences of the United States of America, 2003

309 citations recorded by the source corpus.

There has been a long history of research into the structure and evolution of mankind's scientific endeavor. However, recent progress in applying the tools of science to understand science itself has been unprecedented because only recently has there been access to high-volume and high-quality data sets of scientific output (e.g., publications, patents, grants) and computers and algorithms capable of handling this enormous stream of data. This article reviews major work on models that aim to capture and recreate the structure and dynamics of scientific evolution. We then introduce a general process model that simultaneously grows coauthor and paper citation networks. The statistical and dynamic properties of the networks generated by this model are validated against a 20-year data set of articles published in PNAS. Systematic deviations from a power law distribution of citations to papers are well fit by a model that incorporates a partitioning of authors and papers into topics, a bias for authors to cite recent papers, and a tendency for authors to cite papers cited by papers that they have read. In this TARL model (for topics, aging, and recursive linking), the number of topics is linearly related to the clustering coefficient of the simulated paper citation network.

Subfields: Economics and Econometrics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/aefd211e56c39a89882767382685cc00713f68d0

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Citations in the corpus

Interpretation and trust: designing model-driven visualizations for text analysis (2012, background): “A related literature concerns “science mapping” [6, 7, 8, 32, 40, 42], often via 2D projection of academic citation networks.”
Reputation and impact in academic careers (2013, background): “[27] Börner K, Maru JT, Goldstone RL (2004) The simultaneous”
The science of science: from the perspective of complex systems (2017, methodology): “A framework model that considers the interactions between topics, aging, and recursive follow-up of links (referred to as the TARL model) was proposed as an attempt to describe the simultaneous growth of coauthor and paper citation networks [140].”
Informetrics at the beginning of the 21st century - A review (2008, background): “Börner, Maru, and Goldstone (2004) propose a novel simulation where both author and paper networks evolve simultaneously.”