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Marchi, H. ; Fuchs, C.

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

J. Math. Socio. 48, 100-127 (2024)
Verlagsversion 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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Collaboration Network ; Count Data ; Hurdle Model ; Network Regression ; Qap ; Sparse Networks ; Spatial Proximity
Sprache englisch
Veröffentlichungsjahr 2024
Prepublished im Jahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 0022-250X
e-ISSN 1545-5874
Zeitschrift Journal of Mathematical Sociology
Quellenangaben Band: 48, Heft: 1, Seiten: 100-127 Artikelnummer: , Supplement: ,
Verlag Taylor & Francis
Verlagsort 530 Walnut Street, Ste 850, Philadelphia, Pa 19106 Usa
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-503800-001
Förderungen Deutsche Forschungsgemeinschaft (DFG)
Bielefeld University
DOI 10.1080/0022250X.2023.2180000
WOS ID 000942749300001
Scopus ID 85149484532
Erfassungsdatum 2023-11-29
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