Abstract
Large bibliographic networks are sparse—the average node degree is small. This does not necessarily apply to their product—in some cases, it can “explode” (not sparse, increasing in temporal and spatial complexity). An approach in such cases is to reduce the complexity of the problem by restricting our attention to a selected subset of important nodes and computing with corresponding truncated networks. Nodes can be selected based on various criteria. An option is to consider the most important nodes in the derived network—the nodes with the largest weighted degree. We show that the weighted degrees in a derived network can be efficiently computed without computing the derived network itself, and elaborate on this scheme in detail for some typical cases.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Batagelj, V. (2020). On fractional approach to analysis of linked networks. Scientometrics, 123(2), 621–633. https://doi.org/10.1007/s11192-020-03383-y
Batagelj, V. (2023). GitHub/Bavla/biblio bibmat.R. Retrieved January 16, 2024, from https://github.com/bavla/biblio/tree/master/code
Batagelj, V., & Cerinšek, M. (2013). On bibliographic networks. Scientometrics, 96(3), 845–864. https://doi.org/10.1007/s11192-012-0940-1
Breiger, R. L. (1974). The duality of persons and groups. Social Forces, 53(2), 181–190.
Cerinšek, M., & Batagelj, V. (2018). Semirings and matrix analysis of networks. In R. Alhajj & J. Rokne (Eds.), Encyclopedia of social network analysis and mining (pp. 2379–2386). Springer. https://doi.org/10.1007/978-1-4939-7131-2_152
Chawla, D. S. (2019). Hyperauthorship: Global projects spark surge in thousand-author papers. Nature, NEWS. https://doi.org/10.1038/d41586-019-03862-0
The ATLAS collaboration., Aaboud, M., Aad, G. et al. (2019). Combinations of single-top-quark production cross-section measurements and \(| f_{\text{ LV }} V_{tb}|\) determinations at \(\sqrt{s}= 7\) and 8 TeV with the ATLAS and CMS experiments. Journal of High Energy Physics, 5, 088. https://doi.org/10.1007/JHEP05(2019)088
De Nooy, W., Mrvar, A., & Batagelj, V. (2018). Exploratory social network analysis with Pajek: Revised and expanded edition for updated software (3rd ed.). Cambridge University Press. https://doi.org/10.1017/9781108565691
Garcia, S. R., & Horn, R. A. (2020). Block matrices in linear algebra. PRIMUS, 30(3), 285–306.
Guinness. (2021). Guinness world record 653537: The most authors on a single peer-reviewed academic paper. Retrieved January 16, 2024, from. https://www.guinnessworldrecords.com/world-records/653537-most-authors-on-a-single-peer-reviewed-academic-paper
Maltseva, D., & Batagelj, V. (2020). iMetrics: The development of the discipline with many names. Scientometrics, 125(1), 313–359. https://doi.org/10.1007/s11192-020-03604-4
Matveeva, N. (2023). Scopus data on HKUST publications in years 2017–2019. Private communication.
Newman, M. E. J. (2004). Coauthorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Sciences, 101(suppl_1), 5200–5205. https://doi.org/10.1073/pnas.0307545100
Acknowledgements
The computational work reported in this paper was performed using a collection of R functions bibmat and the program Pajek for analysis of large networks (De Nooy et al., 2018). The code and data are available at Github/Bavla (Batagelj, 2023). This work is supported in part by the Slovenian Research Agency (research program P1-0294, research program CogniCom (0013103) at the University of Primorska, and research projects J5-2557, J1-2481, and J5-4596), and prepared within the framework of the COST action CA21163 (HiTEc). A draft version of this paper was posted on arXiv: https://arxiv.org/abs/2401.04726.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The author declares no Conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix: Computing truncated normalized co-authorship network at level t in Pajek
Appendix: Computing truncated normalized co-authorship network at level t in Pajek
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Batagelj, V. Weighted degrees and truncated derived bibliographic networks. Scientometrics 129, 4863–4883 (2024). https://doi.org/10.1007/s11192-024-05092-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11192-024-05092-2
Keywords
- Collection of bibliographic networks
- Two-mode network
- Derived network
- Co-appearance network
- Fractional approach
- Network normalization
- Truncated network
- Important nodes
- Weighted degree