Advertisement

Author–Coauthor Social Networks and Emerging Scientific Subfields

  • Yasmin H. SaidEmail author
  • Edward J. Wegman
  • Walid K. Sharabati
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

In this paper, we suggest a model of preferential attachment in coauthorship social networks. The process of one actor attaching to another actor (author) and strengthening the tie over time is a stochastic random process based on the distributions of tie-strength and clique size among actors. We will use empirical data to obtain the distributions. The proposed model will be utilized to predict emerging scientific subfields by observing the evolution of the coauthorship network over time. Further, we will examine the distribution of tie-strength of some prominent scholars to investigate the style of coauthorship. Finally, we present an example of a simulated coauthorship network generated randomly to compare with a real-world network.

Notes

Acknowledgements

The work of Dr. Said is supported in part by Grant Number F32AA015876 from the National Institute on Alcohol Abuse and Alcoholism. The work of Dr. Wegman is supported in part by the Army Research Office under contract W911NF-04-1-0447. Both were also supported in part by the Army Research Laboratory under contract W911NF-07-1-0059. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Alcohol Abuse and Alcoholism or the National Institutes of Health.

References

  1. Barabási, A., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512. doi:10.1126/science.286.5439.509.CrossRefMathSciNetGoogle Scholar
  2. Borner, K., Dallasta, L., Ke, W., & Vespignani, A. (2005). Studying the emerging global brain: Analyzing and visualizing the impact of co-authorship teams. Bloomington IN: Indiana University.Google Scholar
  3. Carley, K. (2002). Smart agents and organizations of the future. In L. Lievrouw & S. Livingstone (Eds.), The handbook of new media (Chap. 12, pp. 206–220). Thousand Oaks, CA: Sage.Google Scholar
  4. Cioffi-Revilla, C. (2005). Power laws in the social sciences: Discovering complexity and non-equilibrium dynamics in the social universe. Fairfax, VA: George Mason University.Google Scholar
  5. Krackhardt, D., & Carley, K. (1998). PCANS model of structure in organizations. In Proceedings of the 1998 international symposium on Command and Control Research and Technology (pp. 113–119), Monterey, CA. Vienna, VA: Evidence Based Research.Google Scholar
  6. Roth, C. (2005). Generalized preferential attachment: Towards realistic social network models. In ISWC 4th intl Semantic Web Conference, Workshop on Semantic Network Analysis, Galway, Ireland.Google Scholar
  7. Said, Y., Wegman, E., Sharabati, W., & Rigsby, J. (2008). Social networks of author–coauthor relationships. Computational Statistics and Data Analysis, 52, 2177–2184. doi:10.1016/j.csda.2007.07.021.zbMATHCrossRefMathSciNetGoogle Scholar
  8. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. New York: Cambridge University Press.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Yasmin H. Said
    • 1
    • 2
    Email author
  • Edward J. Wegman
  • Walid K. Sharabati
  1. 1.Isaac Newton Institute for Mathematical SciencesCambridge UniversityCambridgeUK
  2. 2.Department of Computational and Data SciencesGeorge Mason UniversityFairfaxUSA

Personalised recommendations