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Updated: Apr 27, 2026
05:02
Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
Published on: October 24, 2019
33.9K
Expertise finding in bibliographic network: topic dominance learning approach.
IEEE Transactions on Cybernetics
|June 24, 2014
Summary
This study introduces novel methods to identify leading authors in scientific publications by analyzing bibliographic networks. The approach improves expert finding by recognizing key contributors within research groups.
Area of Science:
- Bibliometrics
- Information Science
- Computer Science
Background:
- Expert finding in bibliographic networks is a growing research area.
- Identifying key researchers for specific topics is crucial.
- Authorship contributions in scientific papers are often unequal.
Purpose of the Study:
- To propose discriminative methods for identifying leading authors in scientific publications.
- To simplify expert finding by focusing on identifying experts within research groups.
- To improve the accuracy of expert finding in bibliographic networks.
Main Methods:
- Developed two discriminative methods to identify leading authors.
- Framed expert finding as identifying leading experts in a research group.
- Utilized three distinct feature groups to differentiate key contributors.
Main Results:
- Experimental results demonstrated significant improvements in expert finding performance.
- The proposed methods outperformed existing approaches across common information retrieval metrics.
- Validation was performed on both real-world and synthetic datasets.
Conclusions:
- The proposed methods effectively identify leading authors in scientific publications.
- This approach enhances the precision of expert finding in bibliographic networks.
- The findings offer a more nuanced understanding of authorship and expertise.
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