Increasing trend of scientists to switch between topics

Despite persistent efforts in understanding the creativity of scientists over different career stages, little is known about the underlying dynamics of research topic switching that drives innovation. Here, we analyze the publication records of individual scientists, aiming to quantify their topic switching dynamics and its influence. We find that the co-citing network of papers of a scientist exhibits a clear community structure where each major community represents a research topic. Our analysis suggests that scientists have a narrow distribution of number of topics. However, researchers nowadays switch more frequently between topics than those in the early days. We also find that high switching probability in early career is associated with low overall productivity, yet with high overall productivity in latter career. Interestingly, the average citation per paper, however, is in all career stages negatively correlated with the switching probability. We propose a model that can explain the main observed features. How does a scientist’s tendency to explore a variety of topics affect their career? Here, the authors analyze scientific publication data to understand how often scientists switch topics, how topic switching has changed over time, and how it relates to research productivity.

[1]  Kevin W. Boyack,et al.  Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately? , 2010 .

[2]  J. Macdonall The stay/switch model of concurrent choice. , 2009, Journal of the experimental analysis of behavior.

[3]  Matjaz Perc,et al.  Inheritance patterns in citation networks reveal scientific memes , 2014, ArXiv.

[4]  Carl T. Bergstrom,et al.  The Science of Science , 2018, Science.

[5]  A. Barabasi,et al.  Quantifying the evolution of individual scientific impact , 2016, Science.

[6]  Jie Tang,et al.  ArnetMiner: extraction and mining of academic social networks , 2008, KDD.

[7]  Caleb M Trujillo,et al.  Document co-citation analysis to enhance transdisciplinary research , 2018, Science Advances.

[8]  M E J Newman,et al.  Fast algorithm for detecting community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  Chong Wang,et al.  Continuous Time Dynamic Topic Models , 2008, UAI.

[10]  Santo Fortunato,et al.  Scientific elite revisited: patterns of productivity, collaboration, authorship and impact , 2020, Journal of the Royal Society Interface.

[11]  Graham H. Pyke,et al.  Optimal Foraging: A Selective Review of Theory and Tests , 1977, The Quarterly Review of Biology.

[12]  Vito Latora,et al.  Network dynamics of innovation processes , 2017, Physical review letters.

[13]  Kevin W. Boyack,et al.  Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately? , 2010, J. Assoc. Inf. Sci. Technol..

[14]  Jie Ren,et al.  How to project a bipartite network? , 2007, 0707.0540.

[15]  Michael Szell,et al.  A century of physics , 2015, Nature Physics.

[16]  Martin Rosvall,et al.  Maps of random walks on complex networks reveal community structure , 2007, Proceedings of the National Academy of Sciences.

[17]  Thomas L. Griffiths,et al.  The Author-Topic Model for Authors and Documents , 2004, UAI.

[18]  Yi-Cheng Zhang,et al.  Bipartite network projection and personal recommendation. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[19]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[20]  P. Bourdieu The specificity of the scientific field and the social conditions of the progress of reason , 1975 .

[21]  Andrey Rzhetsky,et al.  Tradition and Innovation in Scientists’ Research Strategies , 2013, ArXiv.

[22]  D J PRICE,et al.  NETWORKS OF SCIENTIFIC PAPERS. , 1965, Science.

[23]  Mônica G. Campiteli,et al.  An index to quantify an individual's scientific research valid across disciplines , 2005 .

[24]  J. E. Hirsch,et al.  An index to quantify an individual's scientific research output , 2005, Proc. Natl. Acad. Sci. USA.

[25]  Alexander Michael Petersen,et al.  Quantifying the impact of weak, strong, and super ties in scientific careers , 2015, Proceedings of the National Academy of Sciences.

[26]  H. Stanley,et al.  The science of science: from the perspective of complex systems , 2017 .

[27]  Albert-László Barabási,et al.  Collective credit allocation in science , 2014, Proceedings of the National Academy of Sciences.

[28]  Vincent D. Blondel,et al.  Career on the Move: Geography, Stratification, and Scientific Impact , 2014, Scientific Reports.

[29]  Alexandre Arenas,et al.  Quantifying the diaspora of knowledge in the last century , 2016, Applied Network Science.

[30]  Yang Wang,et al.  Hot streaks in artistic, cultural, and scientific careers , 2017, Nature.

[31]  Benjamin F. Jones,et al.  Age dynamics in scientific creativity , 2011, Proceedings of the National Academy of Sciences.

[32]  Harry Eugene Stanley,et al.  Persistence and uncertainty in the academic career , 2012, Proceedings of the National Academy of Sciences.

[33]  César A. Hidalgo,et al.  The Product Space Conditions the Development of Nations , 2007, Science.

[34]  Harry Eugene Stanley,et al.  Reputation and impact in academic careers , 2013, Proceedings of the National Academy of Sciences.

[35]  R. Merton The Matthew Effect in Science , 1968, Science.

[36]  Jacob G Foster,et al.  Choosing experiments to accelerate collective discovery , 2015, Proceedings of the National Academy of Sciences.

[37]  Min Song,et al.  Standing on the shoulders of giants , 2017, J. Informetrics.

[38]  M. M. Kessler Bibliographic coupling between scientific papers , 1963 .

[39]  Martin Rosvall,et al.  Estimating the resolution limit of the map equation in community detection. , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

[40]  Alexander M. Petersen,et al.  Multiscale impact of researcher mobility , 2018, Journal of The Royal Society Interface.

[41]  J. Reichardt,et al.  Statistical mechanics of community detection. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[42]  J. March Exploration and exploitation in organizational learning , 1991, STUDI ORGANIZZATIVI.

[43]  K. Kaski,et al.  Limited resolution in complex network community detection with Potts model approach , 2006 .

[44]  An Zeng,et al.  Standing on the shoulders of giants: the effect of outstanding scientists on young collaborators’ careers , 2017, Scientometrics.

[45]  Boleslaw K. Szymanski,et al.  Trends in computer science research , 2013, Commun. ACM.

[46]  M. Heinemann The Matthew Effect , 2016, Thoracic and Cardiovascular Surgeon.

[47]  Ludo Waltman,et al.  A review of the literature on citation impact indicators , 2015, J. Informetrics.

[48]  Boleslaw K. Szymanski,et al.  Quantifying patterns of research-interest evolution , 2017, Nature Human Behaviour.