Modeling the Evolution of Dynamic Triadic Closure Under Superlinear Growth and Node Aging in Citation Networks
Li Liang1, Hao Liu1, Shi-Cai Gong1
1School of Sciences, Zhejiang University of Science and Technology, Hangzhou 310023, China.
Entropy (Basel, Switzerland)
|September 27, 2025
Summary
This study introduces a new model for citation networks, accounting for both growth and aging. The model accurately reproduces real-world network structures, offering insights into knowledge dissemination dynamics.
Area of Science:
- Bibliometrics
- Network Science
- Information Science
Background:
- Citation networks are crucial for understanding knowledge flow.
- Existing models often neglect complex relationships like co-citation.
- Key empirical features include superlinear node inflow and temporal decay of influence.
Purpose of the Study:
- To propose a novel model for citation network evolution.
- To incorporate superlinear growth and aging into network analysis.
- To analyze the interplay between growth and aging in scientific collaboration.
Main Methods:
- Development of the Triangular Evolutionary Model of Superlinear Growth and Aging (TEM-SGA).
- Fitting the TEM-SGA model to empirical citation network data.
- Structural analysis of network properties like degree distributions and clustering coefficients.
- Introduction of a degenerate model (TEM-SG) excluding aging for theoretical analysis.
Main Results:
- The TEM-SGA model successfully reproduces key structural properties of real citation networks.
- Aging's impact on network structure varies with scale and interacts with growth.
- Accelerating growth can mitigate aging-related disruptions in networks.
- The TEM-SG model theoretically predicts power-law distributions for degrees.
Conclusions:
- The study provides a robust model for citation network evolution, considering triadic closure, expansion, and aging.
- Insights into how growth dynamics influence the effects of aging in knowledge dissemination.
- Strengthens the theoretical foundation for analyzing complex citation network structures.
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