Statistical modeling of nanotechnology knowledge diffusion networks

Nanotechnology is crucial for industrial and scientific advancement, with millions of dollars being invested each year in nanotechnology-related research. Recent developments in information-technology enables modeling the knowledge diffusion process via online depositories of nanotechnology-related scientific publication records. Understanding the mechanism may help funding agencies use their funding effectively. This study uses Exponential Random Graph Models (ERGMs), a family of theorygrounded statistical models, to explore the knowledge diffusion patterns among nanotechnology researchers. We systematically evaluate how various attributes of researchers and public funding affect the knowledge diffusion processes. Results show that the impact of public funding on nanotechnology knowledge transfer has been increasing in recent years. Funding all kinds of researchers can stimulate knowledge transfer. Also, funding senior researchers help stimulate knowledge sharing. Our analysis framework of knowledge diffusion networks is effective in studying the knowledge diffusion patterns in nanotechnology, and can be easily applied to other fields.