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Complexity and phase transitions in citation networks: insights from artificial intelligence research
Ariadne A Costa1, Rafael B Frigori2
1Grupo de Redes Complexas Aplicadas de Jataí (GRAJ), Instituto de Ciências Exatas e Tecnológicas, Universidade Federal de Jataí (UFJ), Jataí, GO, Brazi.
Frontiers in Research Metrics and Analytics
|October 10, 2024
Summary
This study reveals that changes in word complexity in artificial intelligence (AI) article titles mirror shifts in AI research and citation network structures, offering insights into scientific evolution.
Area of Science:
- Bibliometrics
- Scientometrics
- Artificial Intelligence (AI) research
Background:
- Scientific progress is often tracked through publication trends and citation networks.
- Understanding the evolution of research topics requires analyzing linguistic and structural changes in scientific literature.
Purpose of the Study:
- To analyze temporal changes in word complexity and citation network structures within artificial intelligence (AI) research up to 2020.
- To investigate the relationship between linguistic complexity in titles and the dynamics of scientific collaboration and knowledge dissemination.
Main Methods:
- Quantitative analysis of word unpredictability in article titles.
- Examination of citation network structures and their evolution over time.
- Correlation analysis between linguistic metrics and network topological features.
Main Results:
- A significant correspondence was observed between fluctuations in word complexity and changes in citation network structures.
- Shifts in thematic focus within AI research are reflected in both title word usage and network connectivity.
- The study demonstrates a link between the evolution of research themes and the underlying network dynamics.
Conclusions:
- The interplay between linguistic complexity and network structure provides a quantitative method for understanding scientific domain evolution.
- This approach can aid in identifying emerging research frontiers and fostering innovation in science.
- The methodology is applicable to studying the progress of scientific fields beyond artificial intelligence.
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