Statistical modeling of nanotechnology knowledge diffusion networks

Shan Jiang, Qiang Gao, Hsinchun Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationInternational Conference on Information Systems (ICIS 2013)
Subtitle of host publicationReshaping Society Through Information Systems Design
Number of pages20
StatePublished - 2013
EventInternational Conference on Information Systems, ICIS 2013 - Milan, Italy
Duration: Dec 15 2013Dec 18 2013

Publication series

NameInternational Conference on Information Systems (ICIS 2013): Reshaping Society Through Information Systems Design


OtherInternational Conference on Information Systems, ICIS 2013


  • Exponential Random Graph Models
  • Knowledge diffusion
  • Statistical network analysis

ASJC Scopus subject areas

  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Applied Mathematics
  • Library and Information Sciences


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