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

Zhang, Y; Wu, M; Miao, W; Huang, L; Lu, J

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

Zhang, Y

Wu, M

Miao, W Huang, L Lu, J

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Journal of Informetrics, 2021, 15, (4)
Issue Date:: 2021-11-01

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Field	Value	Language
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-7731-0301
dc.contributor.author	Wu, M https://orcid.org/0000-0003-3956-7808
dc.contributor.author	Miao, W
dc.contributor.author	Huang, L
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732
dc.date.accessioned	2022-03-21T02:16:44Z
dc.date.available	2022-03-21T02:16:44Z
dc.date.issued	2021-11-01
dc.identifier.citation	Journal of Informetrics, 2021, 15, (4)
dc.identifier.issn	1751-1577
dc.identifier.issn	1875-5879
dc.identifier.uri	http://hdl.handle.net/10453/155404
dc.description.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.
dc.language	English
dc.publisher	ELSEVIER
dc.relation	http://purl.org/au-research/grants/arc/DE190100994
dc.relation.ispartof	Journal of Informetrics
dc.relation.isbasedon	10.1016/j.joi.2021.101202
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0807 Library and Information Studies
dc.subject.classification	Information & Library Sciences
dc.title	Bi-layer network analytics: A methodology for characterizing emerging general-purpose technologies
dc.type	Journal Article
utslib.citation.volume	15
utslib.for	0102 Applied Mathematics
utslib.for	0807 Library and Information Studies
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2022-03-21T02:16:42Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	15
utslib.citation.issue	4

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.

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http://hdl.handle.net/10453/155404