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How to Apply Social Network Analysis to Evaluate Professional Development Programs in Biomedical Research.

Diane B Smith^1,2, Carolyn J Hovde³, Julia Thom Oxford^1,2,4

¹Biomolecular Research Institute, Boise State University, Boise, Idaho.

Current Protocols

|March 9, 2026

Summary

This summary is machine-generated.

Social network analysis (SNA) offers a data-driven method to evaluate research programs by visualizing collaboration dynamics. This approach quantifies program impact and identifies key researchers, aiding resource allocation and strategic development for scientific output.

Keywords:

NIH IDeA programs bibliometrics biomedical research communities co‐authorship network research capacity building programs research program evaluation social network analysis

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Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

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Published on: October 24, 2019

Area of Science:

Bibliometrics
Network Science
Research Program Evaluation

Background:

Evaluating research programs, centers, and institutes presents challenges due to faculty departmentalization and the need for campus-wide resources.
Demonstrating the return on investment for new or emerging research programs can be difficult, especially for institutions with limited research history.

Purpose of the Study:

To outline the methodology for conducting social network analysis (SNA) on bibliometric data from a biomedical research community.
To demonstrate how SNA can be used to assess the collaborative health and impact of research programs.

Main Methods:

Data acquisition from PubMed and processing using VOSviewer for network construction.
Interfacing VOSviewer data with Gephi for advanced network visualization and statistical analysis.
Generating publication-ready figures and creating an author thesaurus for disambiguation.

Main Results:

SNA provides a data-driven understanding of collaboration dynamics, quantifying program impact and return on investment.
Identifies influential researchers (central nodes or bridges) to guide mentorship, grant development, and recruitment.
Highlights productive collaboration areas or potential fragmentation, informing resource allocation and interventions.

Conclusions:

SNA is a valuable tool for evaluating research programs, offering insights into collaboration patterns and network structure.
It establishes a baseline for longitudinal evaluation, tracking changes in collaborative health over time.
The methodology supports informed decision-making for resource allocation, initiative development, and strengthening specific research areas.