Characterizing the potential of being emerging generic technologies: A Bi-Layer Network Analytics-based Prediction Method

Zhang, Y; Zhu, Y; Huang, L; Zhang, G; Lu, J

Characterizing the potential of being emerging generic technologies: A Bi-Layer Network Analytics-based Prediction Method

Zhang, Y

Zhu, Y Huang, L Zhang, G Lu, J

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Publication Type:: Conference Proceeding
Citation:: 17th International Conference on Scientometrics and Informetrics, ISSI 2019 - Proceedings, 2019, 2 pp. 1436 - 1447
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-7731-0301	en_US
dc.contributor.author	Zhu, Y	en_US
dc.contributor.author	Huang, L	en_US
dc.contributor.author	Zhang, G	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.date.issued	2019-01-01	en_US
dc.identifier.citation	17th International Conference on Scientometrics and Informetrics, ISSI 2019 - Proceedings, 2019, 2 pp. 1436 - 1447	en_US
dc.identifier.isbn	9788833811185	en_US
dc.identifier.uri	http://hdl.handle.net/10453/137526
dc.description.abstract	© 2019 17th International Conference on Scientometrics and Informetrics, ISSI 2019 - Proceedings. All rights reserved. Despite tremendous involvement of bibliometrics in profiling technological landscapes and identifying emerging topics, how to predict potential technological change is still unclear. This paper proposes a bi-layer network analytics-based prediction method to characterize the potential of being emerging generic technologies. Initially, based on the innovation literature, three technological characteristics are defined, and quantified by topological indicators in network analytics; a link prediction approach is applied for reconstructing the network with weighted missing links, and such reconstruction will also result in the change of related technological characteristics; the comparison between the two ranking lists of terms can help identify potential emerging generic technologies. A case study on predicting emerging generic technologies in information science demonstrates the feasibility and reliability of the proposed method.	en_US
dc.relation.ispartof	17th International Conference on Scientometrics and Informetrics, ISSI 2019 - Proceedings	en_US
dc.title	Characterizing the potential of being emerging generic technologies: A Bi-Layer Network Analytics-based Prediction Method	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2	en_US
pubs.embargo.period	Not known	en_US
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 - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	2	en_US

Abstract:

© 2019 17th International Conference on Scientometrics and Informetrics, ISSI 2019 - Proceedings. All rights reserved. Despite tremendous involvement of bibliometrics in profiling technological landscapes and identifying emerging topics, how to predict potential technological change is still unclear. This paper proposes a bi-layer network analytics-based prediction method to characterize the potential of being emerging generic technologies. Initially, based on the innovation literature, three technological characteristics are defined, and quantified by topological indicators in network analytics; a link prediction approach is applied for reconstructing the network with weighted missing links, and such reconstruction will also result in the change of related technological characteristics; the comparison between the two ranking lists of terms can help identify potential emerging generic technologies. A case study on predicting emerging generic technologies in information science demonstrates the feasibility and reliability of the proposed method.

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http://hdl.handle.net/10453/137526