Scientific discovery and topological transitions in collaboration networks

We analyze the advent and development of eight scientific fields from their inception to maturity and map the evolution of their networks of collaboration over time, measured in terms of co-authorship of scientific papers. We show that as a field develops it undergoes a topological transition in its collaboration structure between a small disconnected graph to a much larger network where a giant connected component of collaboration appears. As a result, the number of edges and nodes in the largest component undergoes a transition between a small fraction of the total to a majority of all occurrences. These results relate to many qualitative observations of the evolution of technology and discussions of the “structure of scientific revolutions”. We analyze this qualitative change in network topology in terms of several quantitative graph theoretical measures, such as density, diameter, and relative size of the network's largest component.

[1]  Michael B. Usher,et al.  Science in action , 1993, Nature.

[2]  Joseph T Rouse,et al.  Knowledge and Power: Toward a Political Philosophy of Science , 1987 .

[3]  Jean Pierre Courtial,et al.  Co-word analysis as a tool for describing the network of interactions between basic and technological research: The case of polymer chemsitry , 1991, Scientometrics.

[4]  M. Newman,et al.  The structure of scientific collaboration networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[5]  James A. Weisheipl.  Critical Problems in the History of Science , 1960 .

[6]  S. Havlin,et al.  Scale-free networks are ultrasmall. , 2002, Physical review letters.

[7]  Kevin W. Boyack,et al.  Mapping the backbone of science , 2004, Scientometrics.

[8]  Illés Farkas,et al.  Statistical mechanics of topological phase transitions in networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  The Epidemiology of Knowledge and the Spread of Scientific Information , 1980 .

[10]  Weimao Ke,et al.  Studying the emerging global brain: Analyzing and visualizing the impact of co-authorship teams , 2005, Complex..

[11]  Michael J. Singer,et al.  Enhancing the Postdoctoral Experience for Scientists and Engineers , 2001 .

[12]  Richard N. Manning,et al.  World Changes: Thomas Kuhn and the Nature of Science , 1994 .

[13]  S. N. Dorogovtsev,et al.  Self-organization of collaboration networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  Ed C. M. Noyons,et al.  Using bibliometric maps to visualise term distribution in scientific papers , 2002, Proceedings Sixth International Conference on Information Visualisation.

[15]  Adrian Wüthrich,et al.  Drawing Theories Apart: The Dispersion of Feynman Diagrams in Postwar Physics. David Kaiser. University of Chicago Press, Chicago (2005) (xix+469pp., $30.00, Paperback, ISBN: 0-226-42267-4). , 2007 .

[16]  WILLIAM GOFFMAN,et al.  Mathematical Approach to the Prediction of Scientific Discovery , 1971, Nature.

[17]  WILLIAM GOFFMAN,et al.  Generalization of Epidemic Theory: An Application to the Transmission of Ideas , 1964, Nature.

[18]  Albert N. Tabah,et al.  Literature Dynamics: Studies on Growth, Diffusion, and Epidemics , 1999 .

[19]  Henk F. Moed,et al.  Mapping of science by combined co-citation and word analysis. II: Dynamical aspects , 1991 .

[20]  Anthony F. J. van Raan,et al.  On Growth, Ageing, and Fractal Differentiation of Science , 2000, Scientometrics.

[21]  Susan Leigh Star,et al.  Changing Order: Replication and Induction in Scientific Practice by H. M. Collins (review) , 1988, Technology and Culture.

[22]  Carlos Castillo-Chavez,et al.  Population modeling of the emergence and development of scientific fields , 2008, Scientometrics.

[23]  Katy Börner,et al.  Mapping knowledge domains , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[24]  Mark Gerstein,et al.  RNAi Development , 2007, PLoS Comput. Biol..

[25]  David Kaiser.  Drawing Theories Apart: The Dispersion of Feynman Diagrams in Postwar Physics , 2005 .

[26]  L. Bettencourt,et al.  The power of a good idea: Quantitative modeling of the spread of ideas from epidemiological models , 2005, physics/0502067.

[27]  M. Newman,et al.  Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[28]  T. Broadbent,et al.  Criticism and the Growth of Knowledge , 1972 .

[29]  M. Newman.  Coauthorship networks and patterns of scientific collaboration , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[30]  P. Galison.  Refections on Image and Logic: A Material Culture of Microphysics , 1999, Perspectives on Science.

[31]  T. Kuhn,et al.  The Structure of Scientific Revolutions. , 1964 .

[32]  Theodore Arabatzis,et al.  Representing Electrons: A Biographical Approach to Theoretical Entities , 2005 .

[33]  Daniel A. Keim,et al.  On Knowledge Discovery and Data Mining , 1997 .

[34]  D. Price.  Little Science, Big Science , 1965 .

[35]  A. Barabasi,et al.  Evolution of the social network of scientific collaborations , 2001, cond-mat/0104162.

[36]  M. Newman.  1 Who is the best connected scientist ? A study of scientific coauthorship networks , 2004 .

[37]  Michael A. Rodriguez,et al.  Clickstream Data Yields High-Resolution Maps of Science , 2009, PloS one.

[38]  WILLIAM GOFFMAN,et al.  Mathematical Approach to the Spread of Scientific Ideas—the History of Mast Cell Research , 1966, Nature.

[39]  Henk F. Moed,et al.  Mapping of science by combined co-citation and word analysis: II: Dynamical aspects , 1991, J. Am. Soc. Inf. Sci..

[40]  Jean-Philippe Cointet,et al.  Bottom-up scientific field detection for dynamical and hierarchical science mapping, methodology and case study , 2008, Scientometrics.

[41]  Marshall Clagett,et al.  Critical Problems in the History of Science , 1959 .

[42]  Chaomei Chen,et al.  Searching for intellectual turning points: Progressive knowledge domain visualization , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[43]  R. Kohler,.  Lords of the fly: Drosophila genetics and the experimental life. , 1995 .

[44]  Jon M. Kleinberg,et al.  Bursty and Hierarchical Structure in Streams , 2002, Data Mining and Knowledge Discovery.

[45]  S. Redner.  Citation statistics from 110 years of physical review , 2005, physics/0506056.

[46]  Imre Lakatos,et al.  Criticism and the Growth of Knowledge , 1972 .

[47]  Christos Faloutsos,et al.  Graphs over time: densification laws, shrinking diameters and possible explanations , 2005, KDD '05.

[48]  B. Uzzi,et al.  Collaboration and Creativity: The Small World Problem1 , 2005, American Journal of Sociology.

[49]  M E Newman,et al.  Scientific collaboration networks. I. Network construction and fundamental results. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

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