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The simultaneous evolution of author and paper networks
Katy Börner1, Jeegar T Maru, Robert L Goldstone
1School of Library and Information Science, Indiana University, Bloomington, IN 47405, USA. katy@indiana.edu
Summary
This study introduces a new model for understanding scientific evolution by analyzing publication and citation networks. The TARL model (topics, aging, and recursive linking) accurately captures the dynamics of scientific progress.
Area of Science:
- Bibliometrics
- Scientometrics
- Network Science
Background:
- Scientific endeavor evolution has been studied historically.
- Recent advancements in data science and computational power enable new analyses of scientific output.
- High-volume datasets of publications, patents, and grants are now accessible.
Purpose of the Study:
- To review existing models of scientific evolution.
- To introduce a novel process model for simulating scientific networks.
- To validate the model against real-world scientific data.
Main Methods:
- Review of major models for scientific evolution.
- Development of a general process model for coauthor and citation networks.
- Validation using a 20-year dataset from PNAS publications.
- Statistical analysis of network properties and citation distributions.
Main Results:
- The proposed model successfully recreates the structure and dynamics of scientific evolution.
- Systematic deviations from power-law citation distributions were observed.
- The TARL model (topics, aging, and recursive linking) effectively explains these deviations.
- A linear relationship was found between the number of topics and the clustering coefficient in simulated networks.
Conclusions:
- The TARL model provides a robust framework for understanding scientific dynamics.
- Incorporating topics, author aging, and recursive linking is crucial for accurate modeling.
- The study highlights the power of data-driven approaches in scientometrics.