Bi-layer network analytics: A methodology for characterizing emerging general-purpose technologies

Despite the tremendous contributions bibliometrics has made to profiling technological landscapes and identifying emerging topics, reliable methods for predicting potential technological changes are still elusive. To fill this gap, we propose a methodology based on bi-layer network analytics that characterizes emerging general-purpose technologies. The framework incorporates three novel indicators that quantify a technology's technical potential and social impacts, not just in one specific technological area but in a wide range of domains. Missing links in the network are extrapolated through a refined link prediction method, and a weighted resource allocation index ranks both current technologies and their predicted evolutions to reveal candidate innovations for further empirical and/or expert analysis. A case study on information science incorporating quanlitative and qualitative validations demonstrates the methodology to be feasible and reliable. Researchers and policymakers in information science and bibliometrics should find valuable decision support from the empirical insights presented.

Automated summary

This study introduces a methodology based on bi-layer network analytics to characterize emerging general-purpose technologies, quantifying a technology's potential and impact across various domains. By extrapolating missing links in the network and ranking technologies and their predicted evolutions, it identifies candidate innovations for further analysis. Qualitative and quantitative validations in a case study on information science demonstrate the feasibility and reliability of the methodology, providing valuable decision support for researchers and policymakers in the field.