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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.