Performance Information Analysis Using Social Network Analysis (2014)
[Customer]
- Government Agencies
[Key Objectives]
- Introduce Social Network Analysis (SNA) techniques to evaluate R&D performance data in response to R&D project assessments.
- Develop strategies for the dissemination of performance information and establish a service system for future use.
- Ensure effective use of performance information by policy makers and researchers.
[Key Activities]
Data)
- Project Information
• Basic Project Information: Project number, project name, research period, performing organization details, etc.
• Project Fields Information
• Project Summary Information: Research objectives, content, expected outcomes, keywords, etc.
• Research Funding Information
• Participating Researchers Information
- Performance Information
• Papers
• Intellectual Property Rights
• Royalties
• Commercialization
Analysis Process)
1. Data Modeling
- Network Between Research Fields
• Intuitively observe changes in trends of research technology connections and dissemination.
- Network Between Researchers
Category | Node | Link | Remarks |
Network Between Research Fields | Research Fields | Degree of Co-occurrence of Projects Between Research Fields |
|
Network Between Researchers | Researchers | Co-authorship Relationship | Assign separate weights based on the first author/co-author status (reflecting research contribution) |
2. Data Analysis
- Network Between Research Fields
Category | Main Node | Sub Node | Link |
Network Between Research Fields | Research Fields | Projects | Project-Research Field Inclusion Relationship |
1) Network Data Construction (2-mode)
2) Extraction of 1-mode Network from 2-mode Network
3) Measuring Similarity Between Projects and Research Fields (Cosine Similarity)
4) Removal of Self Links and Link Reduction
5) Analysis of Research Field Cohesion Groups (Clique)
- Network Between Researchers
Category | Main Node | Sub Node | Link |
Network Between Researchers | Researchers | Papers | Researcher-Paper Participation Relationship |
1) Network Data Construction (2-mode)
2) Extraction of 1-mode Network from 2-mode Network
3) Measuring Similarity Between Papers and Researchers (Inner Product)
4) Removal of Self Links and Link Reduction
5) Analysis of Researcher Network Centrality and Community Cohesion
* Data Analysis: Using NetMiner
- Visualize the hidden relationships between researchers and research fields formed through R&D projects and outcomes, making it easier for future researchers to collaborate with potential co-researchers, while also identifying trends between research fields.
- This will enable policymakers to track trends between research fields, analyze the status of technological connections between fields, and help researchers identify key individuals among their peers.
ð Identify the ripple effects of research outcomes and utilize them for setting future research directions.
Photo: Pinterest by Mircea Babagianu