Reference : Innovation Networks from Inter-organizational Research Collaborations
Scientific journals : Article
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/45895
Innovation Networks from Inter-organizational Research Collaborations
English
Esmaeilzadeh Dilmaghani, Saharnaz mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > PCOG >]
Piyatumrong, Apivadee mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Danoy, Grégoire mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
Bouvry, Pascal mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
Brust, Matthias R. mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > PCOG >]
16-Dec-2020
Heuristics for Optimization and Learning
Springer, Cham
361-375
Yes
International
[en] We consider the problem of automatizing network generation from inter-organizational research collaboration data. The resulting networks promise to obtain crucial advanced insights. In this paper, we propose a method to convert relational data to a set of networks using a single parameter, called Linkage Threshold (LT). To analyze the impact of the LT-value, we apply standard network metrics such as network density and centrality measures on each network produced. The feasibility and impact of our approach are demonstrated by using a real-world collaboration data set from an established research institution. We show how the produced network layers can reveal insights and patterns by presenting a correlation matrix.
Researchers ; Professionals ; Students
http://hdl.handle.net/10993/45895

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