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Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
Published on: October 24, 2019
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Mining author relationship in scholarly networks based on tripartite citation analysis
Feifei Wang1, Xiaohan Wang1, Siluo Yang2
1School of Economics and Management, Beijing University of Technology, Beijing, China.
Plos One
|November 9, 2017
Summary
This study introduces five author relationship networks, revealing that potential communication relationships (PCR) based on similar themes, like author bibliographic coupling (ABC), can predict future collaborations. These findings aid in discovering new academic communities.
Area of Science:
- Bibliometrics
- Scientometrics
- Information Science
Background:
- Scholarly networks are crucial for understanding academic collaboration and knowledge dissemination.
- Existing methods often focus on direct interactions, potentially missing deeper, indirect relationships.
Purpose of the Study:
- To develop and evaluate multiple author relationship networks for identifying potential and actual academic collaborations.
- To explore the predictive power of different network types for future academic exchanges.
- To introduce an author-relation mining process for uncovering latent scholarly connections.
Main Methods:
- Development of five author relationship networks: co-authorship, author co-citation (AC), author bibliographic coupling (ABC), author direct citation (ADC), and author keyword coupling (AKC).
- Analysis of data across two time periods (T1: before 2011, T2: after 2011) using Quadratic Assignment Procedure.
- Tripartite citation analysis incorporating AC, ABC, and ADC for author-relation mining.
Main Results:
- Identified authors with potential communication relationships (PCR) via ABC or AC, who lack actual communication relationships (ACR).
- Found high correlation between PCR and AKC, and consistency between older PCR and newer ACR, suggesting PCR predicts future interactions.
- Demonstrated that ABC is advantageous in predicting potential scholarly relations.
- Showcased the effectiveness of the author-relation mining process in detecting deep and potential author relationships.
Conclusions:
- Potential communication relationships, particularly those derived from author bibliographic coupling, effectively predict future academic exchanges based on thematic similarity.
- The developed author-relation mining process, utilizing tripartite citation analysis, enhances the discovery of latent author connections and potential collaborations.
- These methods offer a complementary approach to traditional analyses, aiding in the identification of authors with similar research interests and the mapping of emerging academic communities.
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