This work is licensed under the Creative Commons Attribution 4.0 International License.
Asif, R., & Islam, M. A. (2016, April). Finding most collaborating mathematicians a co-author network analysis of mathematics domain. In 2016 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube) (pp. 289–293). IEEE.AsifR.IslamM. A. (2016, April). Finding most collaborating mathematicians a co-author network analysis of mathematics domain. In 2016 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube) (pp. 289–293). IEEE.Search in Google Scholar
Barabâsi, 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.BarabâsiA.-L.JeongH.NédaZ.RavaszE.SchubertA.VicsekT. (2002). Evolution of the social network of scientific collaborations. Physica A: Statistical Mechanics and its Applications, 311(3-4), 590–614.Search in Google Scholar
Barber, M. J., & Scherngell, T. (2013). Is the European R&D network homogeneous? Distinguishing relevant network communities using graph theoretic and spatial interaction modelling approaches. Regional studies, 47(8), 1283–1298.BarberM. J.ScherngellT. (2013). Is the European R&D network homogeneous? Distinguishing relevant network communities using graph theoretic and spatial interaction modelling approaches. Regional studies, 47(8), 1283–1298.Search in Google Scholar
Clauset, A., Newman, M. E., & Moore, C. (2004). Finding community structure in very large networks. Physical Review E—Statistical, Nonlinear, and Soft Matter Physics, 70(6), 066111.ClausetA.NewmanM. E.MooreC. (2004). Finding community structure in very large networks. Physical Review E—Statistical, Nonlinear, and Soft Matter Physics, 70(6), 066111.Search in Google Scholar
Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695, 1–9.CsardiG.NepuszT. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695, 1–9.Search in Google Scholar
Danon, L., Diaz-Guilera, A., Duch, J., & Arenas, A. (2005). Comparing community structure identification. Journal of Statistical Mechanics: Theory and Experiment, 2005(09), P09008.DanonL.Diaz-GuileraA.DuchJ.ArenasA. (2005). Comparing community structure identification. Journal of Statistical Mechanics: Theory and Experiment, 2005(09), P09008.Search in Google Scholar
Falih, I., Grozavu, N., Kanawati, R., & Bennani, Y. (2018). Anca: Attributed network clustering algorithm. In Complex Networks & Their Applications VI: Proceedings of Complex Networks 2017 (The Sixth International Conference on Complex Networks and Their Applications) (pp. 241–252). Springer International Publishing.FalihI.GrozavuN.KanawatiR.BennaniY. (2018). Anca: Attributed network clustering algorithm. In Complex Networks & Their Applications VI: Proceedings of Complex Networks 2017 (The Sixth International Conference on Complex Networks and Their Applications) (pp. 241–252). Springer International Publishing.Search in Google Scholar
Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486(3-5), 75–174.FortunatoS. (2010). Community detection in graphs. Physics Reports, 486(3-5), 75–174.Search in Google Scholar
Fortunato, S., & Hric, D. (2016). Community detection in networks: A user guide. Physics Reports, 659(11), 1–44.FortunatoS.HricD. (2016). Community detection in networks: A user guide. Physics Reports, 659(11), 1–44.Search in Google Scholar
Gaskó, N., Lung, R. I., & Suciu, M. A. (2016). A new network model for the study of scientific collaborations: Romanian computer science and mathematics co-authorship networks. Scientometrics, 108, 613–632.GaskóN.LungR. I.SuciuM. A. (2016). A new network model for the study of scientific collaborations: Romanian computer science and mathematics co-authorship networks. Scientometrics, 108, 613–632.Search in Google Scholar
Groeneveld, R. A., & Meeden, G. (1984). Measuring skewness and kurtosis. Journal of the Royal Statistical Society Series D: The Statistician, 33(4), 391–399.GroeneveldR. A.MeedenG. (1984). Measuring skewness and kurtosis. Journal of the Royal Statistical Society Series D: The Statistician, 33(4), 391–399.Search in Google Scholar
Grossman, J. W. (2002). Patterns of collaboration in mathematical research. SIAM News, 35(9), 8–9.GrossmanJ. W. (2002). Patterns of collaboration in mathematical research. SIAM News, 35(9), 8–9.Search in Google Scholar
Guimerà, R., Uzzi, B., Spiro, J., & Amaral, L. A. N. (2005). Team assembly mechanisms determine collaboration network structure and team performance. Science, 308(5722), 697–702.GuimeràR.UzziB.SpiroJ.AmaralL. A. N. (2005). Team assembly mechanisms determine collaboration network structure and team performance. Science, 308(5722), 697–702.Search in Google Scholar
Harris, M. J., Murtfeldt, R., Wang, S., Mordecai, E. A., & West, J. D. (2023). The role and influence of perceived experts in an anti-vaccine misinformation community. medRxiv.HarrisM. J.MurtfeldtR.WangS.MordecaiE. A.WestJ. D. (2023). The role and influence of perceived experts in an anti-vaccine misinformation community. medRxiv.Search in Google Scholar
Huang, Y., Cheng, X., Tian, C., Jiang, X., Ma, L., & Ma, Y. (2024). Talent hat, cross-border mobility, and career development in China. Quantitative Science Studies, 1–24.HuangY.ChengX.TianC.JiangX.MaL.MaY. (2024). Talent hat, cross-border mobility, and career development in China. Quantitative Science Studies, 1–24.Search in Google Scholar
Huang, Y., Tian, C., & Ma, Y. (2023). Practical operation and theoretical basis of difference-indifference regression in science of science: The comparative trial on the scientific performance of Nobel laureates versus their coauthors. Journal of Data and Information Science, 8(1), 29–46.HuangY.TianC.MaY. (2023). Practical operation and theoretical basis of difference-indifference regression in science of science: The comparative trial on the scientific performance of Nobel laureates versus their coauthors. Journal of Data and Information Science, 8(1), 29–46.Search in Google Scholar
Izquierdo, I., Vessuri, H., & Gonzalez, R. (2018). Scientific collaboration networks of mathematicians from the former soviet union in the global south. Journal of Education and Human Development, 7(4), 83–93.IzquierdoI.VessuriH.GonzalezR. (2018). Scientific collaboration networks of mathematicians from the former soviet union in the global south. Journal of Education and Human Development, 7(4), 83–93.Search in Google Scholar
Klein, J. T. (2005). Interdisciplinary teamwork: The dynamics of collaboration and integration. In S. J. Derry, C. D. Schunn, & M. A. Gernsbacher (Eds.), Interdisciplinary Collaboration: An Emerging Cognitive Science (1st ed., pp. 23–50). NY: Psychology Press.KleinJ. T. (2005). Interdisciplinary teamwork: The dynamics of collaboration and integration. In DerryS. J.SchunnC. D.GernsbacherM. A. (Eds.), Interdisciplinary Collaboration: An Emerging Cognitive Science (1st ed., pp. 23–50). NY: Psychology Press.Search in Google Scholar
Laudel, G. (2001). Collaboration, creativity and rewards: Why and how scientists collaborate. International Journal of Technology Management, 22(7-8), 762–781.LaudelG. (2001). Collaboration, creativity and rewards: Why and how scientists collaborate. International Journal of Technology Management, 22(7-8), 762–781.Search in Google Scholar
Liu, F., Xue, S., Wu, J., Zhou, C., Hu, W., Paris, C., Nepal, S., Yang, J., & Yu, P. S. (2020). Deep learning for community detection: progress, challenges and opportunities. arXiv preprint arXiv:2005.08225.LiuF.XueS.WuJ.ZhouC.HuW.ParisC.NepalS.YangJ.YuP. S. (2020). Deep learning for community detection: progress, challenges and opportunities. arXiv preprint arXiv:2005.08225.Search in Google Scholar
Mao, J., Cao, Y., Lu, K., & Li, G. (2017). Topic scientific community in science: A combined perspective of scientific collaboration and topics. Scientometrics, 112, 851–875.MaoJ.CaoY.LuK.LiG. (2017). Topic scientific community in science: A combined perspective of scientific collaboration and topics. Scientometrics, 112, 851–875.Search in Google Scholar
Newman, M. E. (2001). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences, 98(2), 404–409.NewmanM. E. (2001). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences, 98(2), 404–409.Search in Google Scholar
Newman, M. E. (2004). Coauthorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Sciences, 101(suppl_1), 5200–5205.NewmanM. E. (2004). Coauthorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Sciences, 101(suppl_1), 5200–5205.Search in Google Scholar
Ng, A., Jordan, M., & Weiss, Y. (2001). On spectral clustering: Analysis and an algorithm. In T. Dietterich, S. Becker, & Z. Ghahramani (Eds.), Advances in Neural Information Processing Systems 14 (NIPS 2001).NgA.JordanM.WeissY. (2001). On spectral clustering: Analysis and an algorithm. In DietterichT.BeckerS.GhahramaniZ. (Eds.), Advances in Neural Information Processing Systems 14 (NIPS 2001).Search in Google Scholar
Potts, J., Hartley, J., Montgomery, L., Neylon, C., & Rennie, E. (2017). A journal is a club: A new economic model for scholarly publishing. Prometheus, 35(1), 75–92.PottsJ.HartleyJ.MontgomeryL.NeylonC.RennieE. (2017). A journal is a club: A new economic model for scholarly publishing. Prometheus, 35(1), 75–92.Search in Google Scholar
Priem, J., Piwowar, H., & Orr, R. (2022). OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts. arXiv preprint arXiv:2205.01833.PriemJ.PiwowarH.OrrR. (2022). OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts. arXiv preprint arXiv:2205.01833.Search in Google Scholar
Qin, J., Lancaster, F. W., & Allen, B. (1997). Types and levels of collaboration in interdisciplinary research in the sciences. Journal of the American Society for information Science, 48(10), 893–916.QinJ.LancasterF. W.AllenB. (1997). Types and levels of collaboration in interdisciplinary research in the sciences. Journal of the American Society for information Science, 48(10), 893–916.Search in Google Scholar
Reichardt, J., & Bornholdt, S. (2006). Statistical mechanics of community detection. Physical Review E—Statistical, Nonlinear, and Soft Matter Physics, 74(1), 016110.ReichardtJ.BornholdtS. (2006). Statistical mechanics of community detection. Physical Review E—Statistical, Nonlinear, and Soft Matter Physics, 74(1), 016110.Search in Google Scholar
Rosvall, M., Axelsson, D., & Bergstrom, C. T. (2009). The map equation. The European Physical Journal Special Topics, 178(1), 13–23.RosvallM.AxelssonD.BergstromC. T. (2009). The map equation. The European Physical Journal Special Topics, 178(1), 13–23.Search in Google Scholar
Simpson, E. H. (1949). Measurement of diversity. Nature, 163(4148), 688–688.SimpsonE. H. (1949). Measurement of diversity. Nature, 163(4148), 688–688.Search in Google Scholar
Singh, H., Becattini, N., Cascini, G., & Škec, S. (2021). How familiarity impacts influence in collaborative teams? Proceedings of the Design Society, 1, 1735–1744.SinghH.BecattiniN.CasciniG.ŠkecS. (2021). How familiarity impacts influence in collaborative teams?Proceedings of the Design Society, 1, 1735–1744.Search in Google Scholar
Somerfield, P. J., Clarke, K. R., & Warwick, R. M. (2008). Simpson index. In S. E. Jørgensen & B. D. Fath (Eds.), Encyclopedia of Ecology (pp. 3252–3255). Academic Press.SomerfieldP. J.ClarkeK. R.WarwickR. M. (2008). Simpson index. In JørgensenS. E.FathB. D. (Eds.), Encyclopedia of Ecology (pp. 3252–3255). Academic Press.Search in Google Scholar
Sonnenwald, D. H. (2007). Scientific collaboration. Annual Review of Information Science and Technology, 41(1), 643–681.SonnenwaldD. H. (2007). Scientific collaboration. Annual Review of Information Science and Technology, 41(1), 643–681.Search in Google Scholar
Tomassini, M., & Luthi, L. (2007). Empirical analysis of the evolution of a scientific collaboration network. Physica A: Statistical Mechanics and its Applications, 385(2), 750–764.TomassiniM.LuthiL. (2007). Empirical analysis of the evolution of a scientific collaboration network. Physica A: Statistical Mechanics and its Applications, 385(2), 750–764.Search in Google Scholar
Van Nguyen, M., Kirley, M., & García-Flores, R. (2012). Community evolution in a scientific collaboration network. 2012 IEEE congress on evolutionary computation,Van NguyenM.KirleyM.García-FloresR. (2012). Community evolution in a scientific collaboration network. 2012 IEEE congress on evolutionary computation,Search in Google Scholar
Williams, K., Michalska, S., Cohen, E., Szomszor, M., & Grant, J. (2023). Exploring the application of machine learning to expert evaluation of research impact. Plos one, 18(8), e0288469.WilliamsK.MichalskaS.CohenE.SzomszorM.GrantJ. (2023). Exploring the application of machine learning to expert evaluation of research impact. Plos one, 18(8), e0288469.Search in Google Scholar
Xu, H., Liu, M., Bu, Y., Sun, S., Zhang, Y., Zhang, C., Acuna, D. E., Gray, S., Meyer, E., & Ding, Y. (2024). The impact of heterogeneous shared leadership in scientific teams. Information Processing & Management, 61(1), 103542.XuH.LiuM.BuY.SunS.ZhangY.ZhangC.AcunaD. E.GrayS.MeyerE.DingY. (2024). The impact of heterogeneous shared leadership in scientific teams. Information Processing & Management, 61(1), 103542.Search in Google Scholar
Yu, S., Xia, F., Zhang, C., Wei, H., Keogh, K., & Chen, H. (2021). Familiarity-based collaborative team recognition in academic social networks. IEEE Transactions on Computational Social Systems, 9(5), 1432–1445.YuS.XiaF.ZhangC.WeiH.KeoghK.ChenH. (2021). Familiarity-based collaborative team recognition in academic social networks. IEEE Transactions on Computational Social Systems, 9(5), 1432–1445.Search in Google Scholar
Zhang, X.-S., Wang, R.-S., Wang, Y., Wang, J., Qiu, Y., Wang, L., & Chen, L. (2009). Modularity optimization in community detection of complex networks. Europhysics Letters, 87(3), 38002.ZhangX.-S.WangR.-S.WangY.WangJ.QiuY.WangL.ChenL. (2009). Modularity optimization in community detection of complex networks. Europhysics Letters, 87(3), 38002.Search in Google Scholar
Zhang, Y., Pan, R., Wang, H., & Su, H. (2023). Community detection in attributed collaboration network for statisticians. Stat, 12(1), e507.ZhangY.PanR.WangH.SuH. (2023). Community detection in attributed collaboration network for statisticians. Stat, 12(1), e507.Search in Google Scholar
Zhao, Y., Karypis, G., & Fayyad, U. (2005). Hierarchical clustering algorithms for document datasets. Data mining and knowledge discovery, 10, 141–168.ZhaoY.KarypisG.FayyadU. (2005). Hierarchical clustering algorithms for document datasets. Data mining and knowledge discovery, 10, 141–168.Search in Google Scholar