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Modeling scientific-citation patterns and other triangle-rich acyclic networks
1Department of Physics, Umeå University, 901 87 Umeå, Sweden. zhi-xi.wu@physics.umu.se
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|November 13, 2009
Summary
We developed a model for scientific citation network evolution. Our findings suggest paper impact decreases proportionally to the time since publication, influencing citation patterns.
Area of Science:
- Bibliometrics
- Network Science
- Computational Social Science
Background:
- Understanding the evolution of scientific citation networks is crucial for analyzing research impact and trends.
- Existing models often simplify the complex dynamics of paper relevance and citation formation.
Purpose of the Study:
- To propose and validate a novel model for the evolution of scientific citation networks.
- To incorporate key factors like paper aging and citation triangle formation into network dynamics.
Main Methods:
- Developed a model incorporating out-degree distribution, paper aging, and citation triangle formation.
- Compared model predictions against three network structural quantities from an empirical citation dataset.
- Identified optimal parameter values by minimizing discrepancies between model and empirical data.
Main Results:
- A unique optimal parameter point was found, balancing model and empirical data across all tested quantities.
- The optimal parameters indicate that scientific paper impact is inversely proportional to the time elapsed since publication.
- The model successfully captures essential features of citation network evolution.
Conclusions:
- The proposed model provides a robust framework for understanding citation network dynamics.
- Paper aging and the formation of citation triangles are significant drivers of network evolution.
- The inverse relationship between paper impact and time since publication offers insights into research lifecycles.
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