2017/06/21 by Ji-Eun Kim, Kim, Jieun, Christopher L. Magee +1
Business, Management and Accounting · Decision Sciences · Economics, Econometrics and Finance · #Computers and Society (cs.CY) #Digital Libraries (cs.DL) #FOS: Computer and information sciences #FOS: Physical sciences #Firm Innovation and Growth #Innovation Diffusion and Forecasting #Innovation and Knowledge Management #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.1706.07140
openalex publication_date 2017/06/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The purpose of this study is to investigate the structure and evolution of knowledge spillovers across technological domains. Specifically, dynamic patterns of knowledge flow among 29 technological domains, measured by patent citations for eight distinct periods, are identified and link prediction is tested for capability for forecasting the evolution in these cross-domain patent networks. The overall success of the predictions using the Katz metric implies that there is a tendency to generate increased knowledge flows mostly within the set of previously linked technological domains. This study contributes to innovation studies by characterizing the structural change and evolutionary behaviors in dynamic technology networks and by offering the basis for predicting the emergence of future technological knowledge flows.