Abstract
Scientometry is the discipline for measuring the success and influence of scientific work. This is usually done by analyzing scientific networks, most notably, citation networks and co-authorship networks. In our work we are taking another approach: we observe the evolution of individual scientific careers through the lens of social scientific recognition measured by the membership in program committees of conferences and editorial boards of scientific journals. Then we compare the data on program committee membership and editorial board membership with the history of scientific publications to find frequent sequences of events that lead to one’s invitation to a prestigious conference or journal. We call these sequences motives and we define a few distinct classes of such motives. The large body of data harvested from the Web allows us to experimentally verify the validity and benefit of the proposed approach.
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References
Barabási, A., Jeong, H., Néda, Z., Ravasz, E., Schubert, A., Vicsek, T.: Evolution of the social network of scientific collaborations. Physica A: Statistical Mechanics and its Applications 311(3-4), 590–614 (2002)
Ben-David, J., Sullivan, T.: Sociology of science. Annual Review of Sociology 1, 203–222 (1975)
Granovetter, M.: The strength of weak ties. Am. J of Sociology, 1360–1380 (1973)
Hou, H., Kretschmer, H., Liu, Z.: The structure of scientific collaboration networks in scientometrics. Scientometrics 75(2), 189–202 (2008)
Huang, J., Zhuang, Z., Li, J., Giles, C.L.: Collaboration over time: characterizing and modeling network evolution. In: Proc. of the Int. Conference on Web Search and Web Data Mining, WSDM 2008, pp. 107–116. ACM, New York (2008)
Melin, G.: Pragmatism and self-organization: research collaboration on the individual level. Research policy 29(1), 31–40 (2000)
Moody, J.: The structure of a social science collaboration network: Disciplinary cohesion from 1963 to 1999. American Sociological Review 69(2), 213–238 (2004)
Newman, M.E.J.: Scientific collaboration networks. network construction and fundamental results. Phys. Rev. E 64, 016,131 (2001)
Papineau, D.: Philosophy of science. Wiley Online Library (2007)
Pham, M.C., Klamma, R.: The structure of the computer science knowledge network. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 17–24 (2010)
Schubert, A.: The web of scientometrics. Scientometrics 53(1), 3–20 (2002)
Tomassini, M., Luthi, L.: Empirical analysis of the evolution of a scientific collaboration network. Physica A: Statistical Mechanics and its Applications 385(2), 750–764 (2007)
Van Raan, A.: Scientometrics: State-of-the-art. Scientometrics 38(1), 205–218 (1997)
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Matusiak, A., Morzy, M. (2013). How to Become Famous? Motives in Scientific Social Networks. In: Burduk, R., Jackowski, K., Kurzynski, M., Wozniak, M., Zolnierek, A. (eds) Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013. Advances in Intelligent Systems and Computing, vol 226. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00969-8_66
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DOI: https://doi.org/10.1007/978-3-319-00969-8_66
Publisher Name: Springer, Heidelberg
Print ISBN: 978-3-319-00968-1
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