A network coupling approach to detecting hierarchical linkages between science and technology.
Detecting science–technology hierarchical linkages is beneficial for understanding deep interactions between science and technology (S&T). Previous studies have mainly focused on linear linkages between S&T but ignored their structural linkages. In this paper, we propose a network coupling approach...
| Publicado en: | Journal of the Association for Information Science & Technology Vol. 75; no. 2; pp. 167 - 188 |
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| Autores principales: | , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Feb2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=174603960&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 174603960 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23301635 H6JN jtl: Journal of the Association for Information Science & Technology issn: 23301635 maglogo: N pubinfo: dt: Feb2024 vid: 75 iid: 2 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 174603960 173330643 174603960 174603960 10.1002/asi.24847 174603960 ppf: 167 ppct: 21 formats: tig: atl: A network coupling approach to detecting hierarchical linkages between science and technology. aug: au: Meng, Kai Ba, Zhichao Ma, Yaxue Li, Gang affil: Research Institute for Data Management & Innovation, Nanjing University, Suzhou, China sug: subj: Science Technology Neural Networks (Computer) ab: Detecting science–technology hierarchical linkages is beneficial for understanding deep interactions between science and technology (S&T). Previous studies have mainly focused on linear linkages between S&T but ignored their structural linkages. In this paper, we propose a network coupling approach to inspect hierarchical interactions of S&T by integrating their knowledge linkages and structural linkages. S&T knowledge networks are first enhanced with bidirectional encoder representation from transformers (BERT) knowledge alignment, and then their hierarchical structures are identified based on K‐core decomposition. Hierarchical coupling preferences and strengths of the S&T networks over time are further calculated based on similarities of coupling nodes' degree distribution and similarities of coupling edges' weight distribution. Extensive experimental results indicate that our approach is feasible and robust in identifying the coupling hierarchy with superior performance compared to other isomorphism and dissimilarity algorithms. Our research extends the mindset of S&T linkage measurement by identifying patterns and paths of the interaction of S&T hierarchical knowledge. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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