The world of science possesses an inherent inequality in the distribution of research output and impact. Only a small minority of researchers is responsible for the majority of the knowledge production. However, little is known about the factors that might explain the prestige and the working habits of these researchers. In this paper, we therefore examine the coauthorship networks of the most productive European researchers over a 12-year time window, between the years 2007 and 2018. Explicitly, we look at the impact that these collaborative structures have upon the citations of the researchers. We show that highly productive researchers gain benefits in terms of citations by increasing their research output, by embedding themselves in large geographically dispersed coauthorship networks, as well as by publishing with highly cited collaborators. These results substantiate a prestige effect (the best tend to collaborate with the best) that governs the behaviour of the most productive researchers. Our study thus contributes to the currently coalescing literature on profiling the European research elite, and we hope it will be informative to policy-makers in their efforts of driving Europe towards an integrated research area.
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Abbasi, A. (2013). h-Type hybrid centrality measures for weighted networks. Scientometrics, 96(2), 633–640. https://doi.org/10.1007/s11192-013-0959-y.
Abbasi, A., Altmann, J., & Hossain, L. (2011a). Identifying the effects of co-authorship networks on the performance of scholars: A correlation and regression analysis of performance measures and social network analysis measures. Journal of Informetrics, 5(4), 594–607. https://doi.org/10.1016/j.joi.2011.05.007.
Abbasi, A., Altmann, J., & Hwang, J. (2010). Evaluating scholars based on their academic collaboration activities: Two indices, the RC-index and the CC-index, for quantifying collaboration activities of researchers and scientific communities. Scientometrics, 83(1), 1–13. https://doi.org/10.1007/s11192-009-0139-2.
Abbasi, A., Chung, K. S. K., & Hossain, L. (2012a). Egocentric analysis of co-authorship network structure, position and performance. Information Processing and Management, 48(4), 671–679. https://doi.org/10.1016/j.ipm.2011.09.001.
Abbasi, A., Hossain, L., & Leydesdorff, L. (2012b). Betweenness centrality as a driver of preferential attachment in the evolution of research collaboration networks. Journal of Informetrics, 6(3), 403–412. https://doi.org/10.1016/j.joi.2012.01.002.
Abbasi, A., Hossain, L., Uddin, S., & Rasmussen, K. J. R. (2011b). Evolutionary dynamics of scientific collaboration networks: Multi-levels and cross-time analysis. Scientometrics, 89(2), 687–710. https://doi.org/10.1007/s11192-011-0463-1.
Abbasi, A., & Jaafari, A. (2013). Research impact and scholars’ geographical diversity. Journal of Informetrics, 7(3), 683–692. https://doi.org/10.1016/j.joi.2013.04.004.
Abbasi, A., Jalili, M., & Sadeghi-Niaraki, A. (2018). Influence of network-based structural and power diversity on research performance. Scientometrics, 117(1), 579–590. https://doi.org/10.1007/s11192-018-2879-3.
Abramo, G., & D’Angelo, C. A. (2015). The relationship between the number of authors of a publication, its citations and the impact factor of the publishing journal: Evidence from Italy. Journal of Informetrics, 9(4), 746–761. https://doi.org/10.1016/j.joi.2015.07.003.
Abramo, G., D’Angelo, C. A., & Di Costa, F. (2019). The collaboration behavior of top scientists. Scientometrics, 118(1), 215–232. https://doi.org/10.1007/s11192-018-2970-9.
Andrikopoulos, A., Bekiaris, M., & Kostaris, K. (2020). Stars in a small world: Social networks in auditing research. Scientometrics, 122(1), 625–643. https://doi.org/10.1007/s11192-019-03272-z.
Antoniou, G. A., Antoniou, S. A., Georgakarakos, E. I., Sfyroeras, G. S., & Georgiadis, G. S. (2015). Bibliometric analysis of factors predicting increased citations in the vascular and endovascular literature. Annals of Vascular Surgery, 29(2), 286–292. https://doi.org/10.1016/j.avsg.2014.09.017.
Badar, K., Frantz, T. L., & Jabeen, M. (2016). Research performance and degree centrality in co-authorship networks: The moderating role of homophily. Aslib Journal of Information Management, 68(6), 756–771. https://doi.org/10.1108/AJIM-07-2016-0103.
Badar, K., Hite, J. M., & Ashraf, N. (2015). Knowledge network centrality, formal rank and research performance: Evidence for curvilinear and interaction effects. Scientometrics, 105(3), 1553–1576. https://doi.org/10.1007/s11192-015-1652-0.
Badar, K., Hite, J. M., & Badir, Y. F. (2013). Examining the relationship of co-authorship network centrality and gender on academic research performance: the case of chemistry researchers in Pakistan. Scientometrics, 94(2), 755–775. https://doi.org/10.1007/s11192-012-0764-z.
Badar, K., Hite, J. M., & Badir, Y. F. (2014). The moderating roles of academic age and institutional sector on the relationship between co-authorship network centrality and academic research performance. Aslib Journal of Information Management, 66(1), 38–53. https://doi.org/10.1108/AJIM-05-2013-0040.
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. https://doi.org/10.1016/S0378-4371(02)00736-7.
Benevenuto, F., Laender, A. H. F., & Alves, B. L. (2016). The H-index paradox: Your coauthors have a higher H-index than you do. Scientometrics, 106(1), 469–474. https://doi.org/10.1007/s11192-015-1776-2.
Biscaro, C., & Giupponi, C. (2014). Co-authorship and bibliographic coupling network effects on citations. PLoS ONE, 9(6), e99502. https://doi.org/10.1371/journal.pone.0099502.
Bordons, M., Aparicio, J., González-Albo, B., & Díaz-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. https://doi.org/10.1016/j.joi.2014.12.001.
Borjas, G. J., & Doran, K. B. (2015). Which peers matter? The relative impacts of collaborators, colleagues, and competitors. Review of Economics and Statistics, 97(5), 1104–1117. https://doi.org/10.1162/REST_a_00472.
Bornmann, L., & Daniel, H.-D. (2007). Multiple publication on a single research study: Does it pay? The influence of number of research articles on total citation counts in biomedicine. Journal of the American Society for Information Science and Technology, 58(8), 1100–1107. https://doi.org/10.1002/asi.20531.
Bornmann, L., Schier, H., Marx, W., & Daniel, H.-D. (2012). What factors determine citation counts of publications in chemistry besides their quality? Journal of Informetrics, 6(1), 11–18. https://doi.org/10.1016/j.joi.2011.08.004.
Boschini, A., & Sjögren, A. (2007). Is team formation gender neutral? Evidence from coauthorship patterns. Journal of Labor Economics, 25(2), 325–365. https://doi.org/10.1086/510764.
Brass, D. J., Galaskiewicz, J., Greve, H. R., & Tsai, W. (2004). Taking stock of networks and organizations: A multilevel perspective. Academy of Management Journal, 47(6), 795–817. https://doi.org/10.5465/20159624.
Chan, H. F., Önder, A. S., & Torgler, B. (2016). The first cut is the deepest: Repeated interactions of coauthorship and academic productivity in Nobel laureate teams. Scientometrics, 106(2), 509–524. https://doi.org/10.1007/s11192-015-1796-y.
Chessa, A., Morescalchi, A., Pammolli, F., Penner, O., Petersen, A. M., & Riccaboni, M. (2013). Is Europe evolving toward an integrated research area? Science, 339(6120), 650–651. https://doi.org/10.1126/science.1227970.
Collet, F., Robertson, D. A., & Lup, D. (2014). When does brokerage matter? Citation impact of research teams in an emerging academic field. Strategic Organization, 12(3), 157–179. https://doi.org/10.1177/1476127014530124.
Cummings, J. N., & Kiesler, S. (2005). Collaborative research across disciplinary and organizational boundaries. Social Studies of Science, 35(5), 703–722. https://doi.org/10.1177/0306312705055535.
Ding, Y. (2011). Scientific collaboration and endorsement: Network analysis of coauthorship and citation networks. Journal of Informetrics, 5(1), 187–203. https://doi.org/10.1016/j.joi.2010.10.008.
Ductor, L. (2015). Does co-authorship lead to higher academic productivity? Oxford Bulletin of Economics and Statistics, 77(3), 385–407. https://doi.org/10.1111/obes.12070.
Fernández, A., Ferrándiz, E., & León, M. D. (2018). Patterns of academic scientific collaboration at a distance: Evidence from Southern European countries. In M. Jibu & Y. Osabe (Eds.), Scientometrics. Rijeka: InTech. https://doi.org/10.5772/intechopen.77370.
Freeman, R. B., & Huang, W. (2015). Collaborating with people like me: Ethnic coauthorship within the United States. Journal of Labor Economics, 33(S1), S289–S318. https://doi.org/10.1086/678973.
Friemel, T. N. (2015). Opinion leadership| influence versus selection: A network perspective on opinion leadership. International Journal of Communication, 9, 1002–1022.
Gallotti, R., & De Domenico, M. (2019). Effects of homophily and academic reputation in the nomination and selection of Nobel laureates. Scientific Reports, 9(1), 17304. https://doi.org/10.1038/s41598-019-53657-6.
Gazni, A., & Didegah, F. (2011). Investigating different types of research collaboration and citation impact: A case study of Harvard University’s publications. Scientometrics, 87(2), 251–265. https://doi.org/10.1007/s11192-011-0343-8.
Gazni, A., Sugimoto, C. R., & Didegah, F. (2012). Mapping world scientific collaboration: Authors, institutions, and countries. Journal of the American Society for Information Science and Technology, 63(2), 323–335. https://doi.org/10.1002/asi.21688.
Glänzel, W. (2001). Coauthorship patterns and trends in the sciences (1980–1998): A bibliometric study with implications for database indexing and search strategies. Library Trends, 50(3), 461–473.
Glänzel, W. (2014). Analysis of co-authorship patterns at the individual level. Transinformação, 26(3), 229–238. https://doi.org/10.1590/0103-3786201400030001.
González-Alcaide, G., Pinargote, H., & Ramos, J. M. (2020). From cut-points to key players in co-authorship networks: A case study in ventilator-associated pneumonia research. Scientometrics, 123(2), 707–733. https://doi.org/10.1007/s11192-020-03404-w.
Gossart, C., & Özman, M. (2009). Co-authorship networks in social sciences: The case of Turkey. Scientometrics, 78(2), 323–345. https://doi.org/10.1007/s11192-007-1963-x.
Guan, J., Yan, Y., & Zhang, J. (2015a). How do collaborative features affect scientific output? Evidences from wind power field. Scientometrics, 102(1), 333–355. https://doi.org/10.1007/s11192-014-1311-x.
Guan, J., Zhang, J., & Yan, Y. (2015b). The impact of multilevel networks on innovation. Research Policy, 44(3), 545–559. https://doi.org/10.1016/j.respol.2014.12.007.
Guan, J. C., Zuo, K., Chen, K., & Yam, R. C. M. (2016). Does country-level R&D efficiency benefit from the collaboration network structure? Research Policy, 45(4), 770–784. https://doi.org/10.1016/j.respol.2016.01.003.
Hâncean, M.-G., & Perc, M. (2016). Homophily in coauthorship networks of East European sociologists. Scientific Reports, 6, 36152. https://doi.org/10.1038/srep36152.
Hâncean, M.-G., Perc, M., & Lerner, J. (2020). Data from: The coauthorship networks of the most productive European researchers. Zenodo. https://doi.org/10.5281/zenodo.3873772.
Hâncean, M.-G., Perc, M., & Vlăsceanu, L. (2014). Fragmented Romanian sociology: Growth and structure of the collaboration network. PLoS ONE, 9(11), e113271. https://doi.org/10.1371/journal.pone.0113271.
Hausman, J., Hall, B., & Griliches, Z. (1984). Econometric models for count data with an application to the Patents-R&D relationship (No. t0017) (p. t0017). Cambridge, MA: National Bureau of Economic Research. https://doi.org/10.3386/t0017.
Hilbe, J. M. (2011). Negative binomial regression (2nd ed.). Cambridge, New York: Cambridge University Press.
Hoekman, J., Frenken, K., & Tijssen, R. J. W. (2010). Research collaboration at a distance: Changing spatial patterns of scientific collaboration within Europe. Research Policy, 39(5), 662–673. https://doi.org/10.1016/j.respol.2010.01.012.
Hou, H., Kretschmer, H., & Liu, Z. (2008). The structure of scientific collaboration networks in scientometrics. Scientometrics, 75(2), 189–202. https://doi.org/10.1007/s11192-007-1771-3.
Jeong, H., Néda, Z., & Barabási, A. L. (2003). Measuring preferential attachment in evolving networks. Europhysics Letters (EPL), 61(4), 567–572. https://doi.org/10.1209/epl/i2003-00166-9.
Katz, J. S., & Martin, B. R. (1997). What is research collaboration? Research Policy, 26(1), 1–18. https://doi.org/10.1016/S0048-7333(96)00917-1.
Kwiek, M. (2016). The European research elite: A cross-national study of highly productive academics in 11 countries. Higher Education, 71(3), 379–397. https://doi.org/10.1007/s10734-015-9910-x.
Kwiek, M. (2018). High research productivity in vertically undifferentiated higher education systems: Who are the top performers? Scientometrics, 115(1), 415–462. https://doi.org/10.1007/s11192-018-2644-7.
Larivière, V., Gingras, Y., Sugimoto, C. R., & Tsou, A. (2015). Team size matters: Collaboration and scientific impact since 1900. Journal of the Association for Information Science and Technology, 66(7), 1323–1332. https://doi.org/10.1002/asi.23266.
Levitt, J. M., & Thelwall, M. (2011). A combined bibliometric indicator to predict article impact. Information Processing and Management, 47(2), 300–308. https://doi.org/10.1016/j.ipm.2010.09.005.
Li, E. Y., Liao, C. H., & Yen, H. R. (2013). Co-authorship networks and research impact: A social capital perspective. Research Policy, 42(9), 1515–1530. https://doi.org/10.1016/j.respol.2013.06.012.
Liao, C. H. (2011). How to improve research quality? Examining the impacts of collaboration intensity and member diversity in collaboration networks. Scientometrics, 86(3), 747–761. https://doi.org/10.1007/s11192-010-0309-2.
Long, J. A. (2019). jtools: Analysis and presentation of social scientific data. R package version 2.0.1. Retrieved May 31, 2020, from https://cran.r-project.org/package=jtools.
Martín-Alcázar, F., Ruiz-Martínez, M., & Sánchez-Gardey, G. (2019). Assessing social capital in academic research teams: A measurement instrument proposal. Scientometrics, 121(2), 917–935. https://doi.org/10.1007/s11192-019-03212-x.
McCarty, C., Jawitz, J. W., Hopkins, A., & Goldman, A. (2013). Predicting author h-index using characteristics of the co-author network. Scientometrics, 96(2), 467–483. https://doi.org/10.1007/s11192-012-0933-0.
Medina, A. M. (2018). Why do ecologists search for co-authorships? Patterns of co-authorship networks in ecology (1977–2016). Scientometrics, 116(3), 1853–1865. https://doi.org/10.1007/s11192-018-2835-2.
Merton, R. K. (1968). The Matthew Effect in science: The reward and communication systems of science are considered. Science, 159(3810), 56–63. https://doi.org/10.1126/science.159.3810.56.
Moody, J. (2004). The Structure of a social science collaboration network: Disciplinary cohesion from 1963 to 1999. American Sociological Review, 69(2), 213–238. https://doi.org/10.1177/000312240406900204.
Nature. (2019). The top 10 countries that dominate natural-sciences research, 570. https://doi.org/10.1038/d41586-019-01921-0.
Newman, M. E. J. (2001). Clustering and preferential attachment in growing networks. Physical Review E, 64(2), 025102. https://doi.org/10.1103/PhysRevE.64.025102.
Newman, M. E. J. (2004). Coauthorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Sciences, 101(suppl 1), 5200. https://doi.org/10.1073/pnas.0307545100.
O’brien, R. M. (2007). A caution regarding rules of thumb for variance inflation factors. Quality & Quantity, 41(5), 673–690. https://doi.org/10.1007/s11135-006-9018-6.
Parreira, M. R., Machado, K. B., Logares, R., Diniz-Filho, J. A. F., & Nabout, J. C. (2017). The roles of geographic distance and socioeconomic factors on international collaboration among ecologists. Scientometrics, 113(3), 1539–1550. https://doi.org/10.1007/s11192-017-2502-z.
Perc, M. (2010). Growth and structure of Slovenia’s scientific collaboration network. Journal of Informetrics, 4(4), 475–482. https://doi.org/10.1016/j.joi.2010.04.003.
Perc, M. (2014). The Matthew effect in empirical data. Journal of the Royal Society Interface, 11(98), 20140378. https://doi.org/10.1098/rsif.2014.0378.
Petersen, A. M. (2015). Quantifying the impact of weak, strong, and super ties in scientific careers. Proceedings of the National Academy of Sciences, 112(34), E4671–E4680. https://doi.org/10.1073/pnas.1501444112.
Puuska, H.-M., Muhonen, R., & Leino, Y. (2014). International and domestic co-publishing and their citation impact in different disciplines. Scientometrics, 98(2), 823–839. https://doi.org/10.1007/s11192-013-1181-7.
Ronda-Pupo, G. A., & Pham, T. (2018). The evolutions of the rich get richer and the fit get richer phenomena in scholarly networks: The case of the strategic management journal. Scientometrics, 116(1), 363–383. https://doi.org/10.1007/s11192-018-2761-3.
Rotolo, D., & Messeni Petruzzelli, A. (2013). When does centrality matter? Scientific productivity and the moderating role of research specialization and cross-community ties. Journal of Organizational Behavior, 34(5), 648–670. https://doi.org/10.1002/job.1822.
Sarigöl, E., Pfitzner, R., Scholtes, I., Garas, A., & Schweitzer, F. (2014). Predicting scientific success based on coauthorship networks. EPJ Data Science, 3(1), 9. https://doi.org/10.1140/epjds/s13688-014-0009-x.
Scarazzati, S., & Wang, L. (2019). The effect of collaborations on scientific research output: The case of nanoscience in Chinese regions. Scientometrics, 121(2), 839–868. https://doi.org/10.1007/s11192-019-03220-x.
Shoukri, M. M. (2018). Statistical analysis of health data using SAS and R (4th ed.). Boca Raton: CRC Press.
Sidone, O. J. G., Haddad, E. A., & Mena-Chalco, J. P. (2017). Scholarly publication and collaboration in Brazil: The role of geography. Journal of the Association for Information Science and Technology, 68(1), 243–258. https://doi.org/10.1002/asi.23635.
Sugimoto, C. R., Robinson-Garcia, N., Murray, D. S., Yegros-Yegros, A., Costas, R., & Larivière, V. (2017). Scientists have most impact when they’re free to move. Nature, 550(7674), 29–31. https://doi.org/10.1038/550029a.
Sun, L., & Rahwan, I. (2017). Coauthorship network in transportation research. Transportation Research Part A: Policy and Practice, 100, 135–151. https://doi.org/10.1016/j.tra.2017.04.011.
Tu, J. (2019). What connections lead to good scientific performance? Scientometrics, 118(2), 587–604. https://doi.org/10.1007/s11192-018-02997-7.
Uddin, S., Choudhury, N., & Hossain, M. E. (2019). A research framework to explore knowledge evolution and scholarly quantification of collaborative research. Scientometrics, 119(2), 789–803. https://doi.org/10.1007/s11192-019-03057-4.
Venables, W. N., & Ripley, B. D. (2010). Modern applied statistics with S (4 ed., [Nachdr.].). New York: Springer.
Wagner, C. S., Whetsell, T. A., & Leydesdorff, L. (2017). Growth of international collaboration in science: Revisiting six specialties. Scientometrics, 110(3), 1633–1652. https://doi.org/10.1007/s11192-016-2230-9.
Wang, J. (2016). Knowledge creation in collaboration networks: Effects of tie configuration. Research Policy, 45(1), 68–80. https://doi.org/10.1016/j.respol.2015.09.003.
Wang, L., Thijs, B., & Glänzel, W. (2015). Characteristics of international collaboration in sport sciences publications and its influence on citation impact. Scientometrics, 105(2), 843–862. https://doi.org/10.1007/s11192-015-1735-y.
Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge, New York: Cambridge University Press.
Wuchty, S., Jones, B. F., & Uzzi, B. (2007). The Increasing dominance of teams in production of knowledge. Science, 316(5827), 1036–1039. https://doi.org/10.1126/science.1136099.
Yin, Z., & Zhi, Q. (2017). Dancing with the academic elite: A promotion or hindrance of research production? Scientometrics, 110(1), 17–41. https://doi.org/10.1007/s11192-016-2151-7.
Zhang, C., Bu, Y., Ding, Y., & Xu, J. (2018). Understanding scientific collaboration: Homophily, transitivity, and preferential attachment. Journal of the Association for Information Science and Technology, 69(1), 72–86. https://doi.org/10.1002/asi.23916.
Funding was provided by Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii (Grant No. PN-III-P1-1.1-TE- 2016-0362), The Slovenian Research Agency (Grant Nos. J1-2457, J1-9112, and P1-0403) and Deutsche Forschungsgemeinschaft (Grant No. LE 2237/2-1). We thank Ms. Laura Trandafir for her help in formatting Fig. 1.
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Hâncean, MG., Perc, M. & Lerner, J. The coauthorship networks of the most productive European researchers. Scientometrics 126, 201–224 (2021). https://doi.org/10.1007/s11192-020-03746-5
- Highly productive researchers
- Coauthorship networks
- Research productivity