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

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  • Zhang, Yi
  • Wu, Mengjia
  • Miao, Wen
  • Huang, Lu
  • Lu, Jie

Abstract

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.

Suggested Citation

  • Zhang, Yi & Wu, Mengjia & Miao, Wen & Huang, Lu & Lu, Jie, 2021. "Bi-layer network analytics: A methodology for characterizing emerging general-purpose technologies," Journal of Informetrics, Elsevier, vol. 15(4).
    • Handle: RePEc:eee:infome:v:15:y:2021:i:4:s1751157721000730
    DOI: 10.1016/j.joi.2021.101202
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    3. Jung, Sukhwan & Segev, Aviv, 2022. "DAC: Descendant-aware clustering algorithm for network-based topic emergence prediction," Journal of Informetrics, Elsevier, vol. 16(3).
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    5. Zhenyu Yang & Wenyu Zhang & Zhimin Wang & Xiaoling Huang, 2024. "A deep learning-based method for predicting the emerging degree of research topics using emerging index," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(7), pages 4021-4042, July.

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