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Hurdle-QAP models overcome dependency and sparsity in scientific collaboration count networks.

J. Math. Socio. 48, 100-127 (2024)
Publ. Version/Full Text DOI
Open Access Hybrid
Creative Commons Lizenzvertrag
Spatial proximity may facilitate scientific collaboration. We regress its impact within two German research institutions, defining collaboration strength and proximity by the number of joint publications and spatial distance between work places. The methodological focus lies on accounting for (i) the dependency structure in network data and (ii) excess zeros in the sparse target matrix. The former can be addressed by a quadratic assignment procedure (QAP), the second by a hurdle model. To offer a joint solution, we combine the methods to novel parametric and non-parametric hurdle-QAP models. The analysis reveals that proximity can facilitate collaboration, but significant effects get lost within building structures. Outcomes of this study may inform about how to target the promotion of interdisciplinary research.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Collaboration Network ; Count Data ; Hurdle Model ; Network Regression ; Qap ; Sparse Networks ; Spatial Proximity
Language english
Publication Year 2024
Prepublished in Year 2023
HGF-reported in Year 2023
ISSN (print) / ISBN 0022-250X
e-ISSN 1545-5874
Quellenangaben Volume: 48, Issue: 1, Pages: 100-127 Article Number: , Supplement: ,
Publisher Taylor & Francis
Publishing Place 530 Walnut Street, Ste 850, Philadelphia, Pa 19106 Usa
Reviewing status Peer reviewed
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-503800-001
Grants Deutsche Forschungsgemeinschaft (DFG)
Bielefeld University
Scopus ID 85149484532
Erfassungsdatum 2023-11-29