Hostname: page-component-78c5997874-v9fdk Total loading time: 0 Render date: 2024-11-10T17:46:05.445Z Has data issue: false hasContentIssue false
  • English
  • Français

Investigating scientific mobility in co-authorship networks using multilayer temporal motifs

Published online by Cambridge University Press:  07 October 2021

Hanjo D. Boekhout Open the ORCID record for Hanjo D. Boekhout [Opens in a new window] ,
Vincent A. Traag  and
Frank W. Takes
Hanjo D. Boekhout*
Affiliation:
Department of Computer Science (LIACS), Leiden University, The Netherlands (e-mail: f.w.takes@liacs.leidenuniv.nl) Centre for Science & Technology Studies (CWTS), Leiden University, The Netherlands (e-mail: v.a.traag@cwts.leidenuniv.nl)
Vincent A. Traag
Affiliation:
Centre for Science & Technology Studies (CWTS), Leiden University, The Netherlands (e-mail: v.a.traag@cwts.leidenuniv.nl)
Frank W. Takes
Affiliation:
Department of Computer Science (LIACS), Leiden University, The Netherlands (e-mail: f.w.takes@liacs.leidenuniv.nl)
*
*Corresponding author. Email: h.d.boekhout@liacs.leidenuniv.nl
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

This paper introduces a framework for understanding complex temporal interaction patterns in large-scale scientific collaboration networks. In particular, we investigate how two key concepts in science studies, scientific collaboration and scientific mobility, are related and possibly differ between fields. We do so by analyzing multilayer temporal motifs: small recurring configurations of nodes and edges.

Driven by the problem that many papers share the same publication year, we first provide a methodological contribution: an efficient counting algorithm for multilayer temporal motifs with concurrent edges. Next, we introduce a systematic categorization of the multilayer temporal motifs, such that each category reflects a pattern of behavior relevant to scientific collaboration and mobility. Here, a key question concerns the causal direction: does mobility lead to collaboration or vice versa? Applying this framework to scientific collaboration networks extracted from Web of Science (WoS) consisting of up to 7.7 million nodes (authors) and 94 million edges (collaborations), we find that international collaboration and international mobility reciprocally influence one another. Additionally, we find that Social sciences & Humanities (SSH) scholars co-author to a greater extent with authors at a distance, while Mathematics & Computer science (M&C) scholars tend to continue to collaborate within the established knowledge network and organization.

Keywords

co-authorship networksscientific mobilityscientific collaborationmotif countingmultilayer temporal motifsnetwork motifsconcurrent edges
Type
Research Article
Information
Network Science , Volume 9 , Issue 3 , September 2021 , pp. 354 - 386
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2021. Published by Cambridge University Press

Footnotes

Action Editor: Ulrik Brandes

References

Aman, V. (2018). Does the Scopus author ID suffice to track scientific international mobility? A case study based on Leibniz laureates. Scientometrics, 117(2), 705–720.CrossRefGoogle Scholar
Appelt, S., van Beuzekom, B., Galindo-Rueda, F., & de Pinho, R. (2015). Which factors influence the international mobility of research scientists? In Global mobility of research scientists (pp. 177–213). Elsevier.Google Scholar
Artzy-Randrup, Y., Fleishman, S. J., Ben-Tal, N., & Stone, L. (2004). Comment on “Network motifs: Simple building blocks of complex networks” and “Superfamilies of evolved and designed networks”. Science, 305(5687), 1107c.CrossRefGoogle Scholar
Barabási, A.-L. (2016). Network science. Cambridge University Press.Google Scholar
Barabási, A.-L., Jeong, H., Néda, Z., Ravasz, E., Schubert, A., & Vicsek, T. (2002). Evolution of the social network of scientific collaborations. Physica A: Statistical Mechanics and its Applications, 311(3–4), 590–614.CrossRefGoogle Scholar
Baruffaldi, S. H., & Landoni, P. (2010). Effects and determinants of the scientific international mobility: the cases of foreign researchers in Italy and Portugal. In Paper for the Triple Helix VIII conference.Google Scholar
Benson, A. R., Gleich, D. F., & Leskovec, J. (2016). Higher-order organization of complex networks. Science, 353(6295), 163–166.CrossRefGoogle Scholar
Birnholtz, J. P. (2006). What does it mean to be an author? The intersection of credit, contribution, and collaboration in science. Journal of the American Society for Information Science and Technology, 57(13), 1758–1770.CrossRefGoogle Scholar
Boekhout, H. D. (2020). Counting multilayer temporal motifs. Retrieved from https://bitbucket.org/Fractals-/count_mult_temp_motifs, June 4, 2020.Google Scholar
Boekhout, H. D., Kosters, W. A., & Takes, F. W. (2019). Efficiently counting complex multilayer temporal motifs in large-scale networks. Computational Social Networks, 6(1), 1–34.CrossRefGoogle Scholar
Bordons, M., Aparicio, J., González-Albo, B., & Daz-Faes, A. A. (2015). The relationship between the research performance of scientists and their position in co-authorship networks in three fields. Journal of Informetrics, 9(1), 135–144.CrossRefGoogle Scholar
Caron, E., & van Eck, N. Jan. (2014). Large scale author name disambiguation using rule-based scoring and clustering. In Proceedings of the 19th international conference on science and technology indicators (pp. 79–86). CWTS-Leiden University.Google Scholar
Chakrabarty, B., & Parekh, N. (2016). NAPS: Network analysis of protein structures. Nucleic Acids Research, 44(W1), W375–W382.CrossRefGoogle ScholarPubMed
Charbey, R., & Prieur, C. (2019). Stars, holes, or paths across your facebook friends: A graphlet-based characterization of many networks. Network Science, 7(4), 476–497.CrossRefGoogle Scholar
Chinchilla-Rodrguez, Z., Miao, L., Murray, D., Robinson-Garca, N., Costas, R., & Sugimoto, C. R. (2017). Networks of international collaboration and mobility: A comparative study. In Proceedings of the 16th international conference on scientometrics & infometrics. ISSI.Google Scholar
Chinchilla-Rodrguez, Z., Miao, L., Murray, D., Robinson-Garca, N., Costas, R., & Sugimoto, C. R. (2018). A global comparison of scientific mobility and collaboration according to national scientific capacities. Frontiers in Research Metrics and Analytics, 3, 17.CrossRefGoogle Scholar
Choobdar, S., Ribeiro, P., Bugla, S., & Silva, F. (2012). Comparison of co-authorship networks across scientific fields using motifs. In Proceedings of the international conference on advances in social networks analysis and mining (ASONAM) (pp. 147–152). IEEE Computer Society.CrossRefGoogle Scholar
Czaika, M., & Orazbayev, S. (2018). The globalisation of scientific mobility, 1970–2014. Applied Geography, 96, 1–10.CrossRefGoogle Scholar
Das, K., Samanta, S., & Pal, M. (2018). Study on centrality measures in social networks: a survey. Social Network Analysis and Mining, 8(1), 13.CrossRefGoogle Scholar
Gaillard, J., & Gaillard, A. M. (1997). Introduction: the international mobility of brains: exodus or circulation? Science, Technology and Society, 2(2), 195–228.CrossRefGoogle Scholar
Glänzel, W., & Schubert, A. (2005). Domesticity and internationality in co-authorship, references and citations. Scientometrics, 65(3), 323–342.CrossRefGoogle Scholar
Guth, J., & Gill, B. (2008). Motivations in East–West doctoral mobility: Revisiting the question of brain drain. Journal of Ethnic and Migration Studies, 34(5), 825–841.CrossRefGoogle Scholar
Holme, P., & Saramäki, J. (2019). Temporal network theory. Springer.CrossRefGoogle Scholar
Hu, X., Li, O. Z., & Pei, S. (2019). Of stars and galaxies – Co-authorship network and research. China Journal of Accounting Research.Google Scholar
Jazayeri, A., & Yang, C. C. (2020). Motif discovery algorithms in static and temporal networks: A survey. Journal of Complex Networks, 8(4), cnaa031.CrossRefGoogle Scholar
Kato, M., & Ando, A. (2017). National ties of international scientific collaboration and researcher mobility found in Nature and Science. Scientometrics, 110(2), 673–694.CrossRefGoogle Scholar
Krumov, L., Fretter, C., Müller-Hannemann, M., Weihe, K., & Hütt, M-T. (2011). Motifs in co-authorship networks and their relation to the impact of scientific publications. The European Physical Journal B, 84(4), 535–540.CrossRefGoogle Scholar
Kumar, S. (2015). Co-authorship networks: a review of the literature. Aslib Journal of Information Management, 67(1), 55–73.CrossRefGoogle Scholar
Larivière, V., Gingras, Y., & Archambault, É. (2006). Canadian collaboration networks: A comparative analysis of the natural sciences, social sciences and the humanities. Scientometrics, 68(3), 519–533.CrossRefGoogle Scholar
Laudel, G. (2003). Studying the brain drain: Can bibliometric methods help? Scientometrics, 57(2), 215–237.CrossRefGoogle Scholar
Leskovec, J., & Sosič, R. (2016). SNAP: A general-purpose network analysis and graph-mining library. ACM Transactions on Intelligent Systems and Technology (TIST), 8(1), 1.CrossRefGoogle ScholarPubMed
Leyman, A. (2009). Home sweet home? International mobility among Flemish doctoral researchers. In Higher education, partnership, innovation (pp. 67–74). Budapest: IHEPI.Google Scholar
Mali, F., Kronegger, L., Doreian, P., & Ferligoj, A. (2012). Dynamic scientific co-authorship networks. In: Models of science dynamics (pp. 195–232). Springer.Google Scholar
Melin, G., & Persson, O. (1996). Studying research collaboration using co-authorships. Scientometrics, 36(3), 363–377.CrossRefGoogle Scholar
Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., & Alon, U. (2002). Network motifs: Simple building blocks of complex networks. Science, 298(5594), 824–827.CrossRefGoogle ScholarPubMed
Mingers, J., & Leydesdorff, L. (2015). A review of theory and practice in scientometrics. European Journal of Operational Research, 246(1), 1–19.CrossRefGoogle Scholar
Moed, H. F., Aisati, M., & Plume, A. (2013). Studying scientific migration in Scopus. Scientometrics, 94(3), 929–942.CrossRefGoogle Scholar
Molontay, R., & Nagy, M. (2019). Two decades of network science: as seen through the co-authorship network of network scientists. In Proceedings of the 2019 IEEE/ACM international conference on advances in social networks analysis and mining (pp. 578–583).CrossRefGoogle Scholar
Newman, M. E. J. (2004). Coauthorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Sciences, 101(1), 5200–5205.CrossRefGoogle ScholarPubMed
Paranjape, A., Benson, A. R., & Leskovec, J. (2017). Motifs in temporal networks. In Proceedings of the tenth ACM international conference on web search and data mining (WSDM) (pp. 601–610). ACM.CrossRefGoogle Scholar
Paul-Hus, A., Mongeon, P., Sainte-Marie, M., & Larivière, V. (2017). The sum of it all: Revealing collaboration patterns by combining authorship and acknowledgements. Journal of Informetrics, 11(1), 80–87.CrossRefGoogle Scholar
Ribeiro, P., Paredes, P., Silva, M.E. P., Aparicio, D., & Silva, F. (2021). A survey on subgraph counting: Concepts, algorithms, and applications to network motifs and graphlets. ACM Computing Surveys (CSUR), 54(2), 1–36.CrossRefGoogle Scholar
Stark, O., Helmenstein, C., & Prskawetz, A. (1997). A brain gain with a brain drain. Economics Letters, 55(2), 227–234.CrossRefGoogle Scholar
Takes, F. W., Kosters, W. A., Witte, B., & Heemskerk, E. M. (2018). Multiplex network motifs as building blocks of corporate networks. Applied Network Science, 3(1), 39.CrossRefGoogle ScholarPubMed
Wagner, C. S., & Leydesdorff, L. (2005). Network structure, self-organization, and the growth of international collaboration in science. Research Policy, 34(10), 1608–1618.CrossRefGoogle Scholar
Waltman, L., Calero-Medina, C., Kosten, J., Noyons, Ed. C. M., Tijssen, R. J. W., van Eck, N. J., … Wouters, P. (2012). The Leiden Ranking 2011/2012: Data collection, indicators, and interpretation. Journal of the American society for Information Science and Technology, 63(12), 2419–2432.CrossRefGoogle Scholar
Supplementary material: File

Boekhout et al. supplementary material

Boekhout et al. supplementary material

Download Boekhout et al. supplementary material(File)
File 1.7 MB