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Hurdle-QAP models overcome dependency and sparsity in scientific collaboration count networks.
J. Math. Socio. 48, 100-127 (2024)
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
ISSN (print) / ISBN
0022-250X
e-ISSN
1545-5874
Quellenangaben
Volume: 48,
Issue: 1,
Pages: 100-127
Publisher
Taylor & Francis
Publishing Place
530 Walnut Street, Ste 850, Philadelphia, Pa 19106 Usa
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of Computational Biology (ICB)
Grants
Deutsche Forschungsgemeinschaft (DFG)
Bielefeld University
Bielefeld University