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This study models academic citation networks using an inhomogeneous causal network approach. This method accurately captures citation network features like node growth and degree distributions, improving upon homogeneous models.

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Area of Science:

  • Bibliometrics
  • Network Science
  • Cosmology

Background:

  • Citation analysis is crucial for understanding scientific impact.
  • Existing citation network models face limitations in explaining phenomena like 'hot papers'.
  • Network cosmology offers potential analogies for modeling citation networks due to similar degree distributions.

Purpose of the Study:

  • To propose an inhomogeneous causal network model for citation networks.
  • To address the limitations of homogeneous spacetime models in capturing citation dynamics.
  • To develop a model that accurately reflects citation network features.

Main Methods:

  • Treating paper citations as causal relationships.
  • Adapting causal network models from network cosmology.
  • Developing an inhomogeneous causal network model with a novel connection mechanism.

Main Results:

  • The proposed inhomogeneous model effectively captures citation network features.
  • Generated networks exhibit node growth trends and degree distributions similar to real-world citation networks.
  • The connection mechanism aligns well with observed citation patterns.

Conclusions:

  • Inhomogeneous causal network models provide a robust framework for studying citation networks.
  • This approach enhances our understanding of scientific impact and information flow.
  • The model offers a more accurate representation of citation dynamics compared to homogeneous models.