Multilayer network approach to modeling authorship influence on citation dynamics in physics journals

Area of Science:

• Network Science
• Scientometrics
• Computational Social Science

Background:

• Modeling complex systems often requires understanding interactions between different components.
• Scientometric networks, analyzing publications and citations, are crucial for understanding scientific impact.
• Existing models may not fully capture the interplay between social factors and citation dynamics.

Purpose of the Study:

• To develop a general framework for modeling the growth of multilayer networks.
• To quantify the impact of author characteristics (e.g., publication history, collaborations) on citation dynamics.
• To apply and validate this framework to scientometric networks in physics.

Main Methods:

• Constructed a multilayer network model combining citation and social network features.
• Developed a statistical method for parameter estimation and model selection in growing networks.
• Applied the model to citation data from nine physics journals.

Main Results:

• The model successfully integrates author attributes to predict citation patterns.
• Evaluated different combinations of citation and social factors to explain citation dynamics.
• The developed statistical method is computationally efficient and scalable for large networks.

Conclusions:

• Author characteristics significantly impact the citation dynamics of new publications.
• The proposed framework provides a robust tool for analyzing complex, growing multilayer networks.
• This research offers insights into the structure and evolution of scientific communication.