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The Impact of Network Closure and Structural Holes on Research Performance in Collaboration Networks
- narilee@yonsei.ac.kr
Ji-Hong Park (Yonsei University)
- jihongpark@yonsei.ac.kr
Abstract
This study investigates the collaboration networks in the field of AI-driven diagnostic medical imaging, focusing on the influence of two social capital concepts—network closure and structural holes—on research performance. The analysis reveals a highly fragmented network structure with one dominant component, while individual clusters exhibit strong internal cohesion. Both network closure, measured by density, and structural holes, assessed through efficiency, positively impact research performance, as demonstrated by QAP regression analysis. The findings highlight that, in the integration of AI into diagnostic medical imaging, robust connections among researchers are vital, and the presence of structural holes, which enable the assimilation of diverse knowledge, also significantly enhances research outcomes. This underscores the importance of fostering a well-balanced network to optimize collaboration and knowledge production in this emerging interdisciplinary field.
- keywords
- collaboration network, network closure, structural holes, social capital, AI in diagnostic medical imaging
- Submission Date
- 2024-08-16
- Revised Date
- 2024-08-27
- Accepted Date
- 2024-09-03
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