Advertisement

Social inertia and diversity in collaboration networks

  • J. J. Ramasco
Article

Abstract.

Random graphs are useful tools to study social interactions. In particular, the use of weighted random graphs allows to handle a high level of information concerning which agents interact and in which degree the interactions take place. Taking advantage of this representation, we recently defined a magnitude, the Social Inertia, that measures the eagerness of agents to keep ties with previous partners. To study this magnitude, we used collaboration networks that are specially appropriate to obtain valid statitical results due to the large size of publically available databases. In this work, I study the Social Inertia in two of these empirical networks, IMDB movie database and condmat. More specifically, I focus on how the Inertia relates to other properties of the graphs, and show that the Inertia provides information on how the weight of neighboring edges correlates. A social interpretation of this effect is also offered.

Keywords

European Physical Journal Special Topic Random Graph Actor Network Weighted Graph Collaboration Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. R. Albert, A.-L. Barabási, Rev. Mod. Phys. 74, 47 (2002) CrossRefADSMathSciNetGoogle Scholar
  2. S.N. Dorogovtsev, J.F.F. Mendes, Evolution of networks: From Biological Nets to the Internet and WWW (Oxford University Press, Oxford, 2003) Google Scholar
  3. R. Pastor-Satorras, A. Vespignani, Evolution and structure of the Internet: A statistical physics approach (Cambridge University Press, Cambridge, 2004) Google Scholar
  4. M.E.J. Newman, SIAM Review 45, 167 (2003) zbMATHCrossRefMathSciNetGoogle Scholar
  5. A.-L. Barabási, R. Albert, Science 286, 509 (1999) CrossRefMathSciNetGoogle Scholar
  6. R. Albert, H. Jeong, A.-L. Barabási, Nature 401, 130 (1999) CrossRefADSGoogle Scholar
  7. R. Pastor-Satorras, A. Vázquez, A. Vespignani, Phys. Rev. Lett. 87, 258701 (2001) CrossRefADSGoogle Scholar
  8. H. Jeong, B. Tombor, R. Albert, Z.N. Oltvai, A.-L. Barabási, Nature 407, 651 (2000) CrossRefADSGoogle Scholar
  9. B. Schwikowski, P. Uetz, S. Fields, Nat. Biotech. 18, 1257 (2000) CrossRefGoogle Scholar
  10. S. Wuchty, Z.N. Oltvai, A.-L. Barabási, Nat. Genet. 35, 176 (2003) CrossRefGoogle Scholar
  11. S.H. Yook, H. Jeong, A.-L. Barabási, Y. Tu, Phys. Rev. Lett. 86, 5835 (2001) CrossRefADSGoogle Scholar
  12. M.E.J. Newman, Proc. Natl. Acad. Sci. USA 98, 404 (2001); Phys. Rev. E 64, 016131 and 016132 (2001) zbMATHCrossRefADSMathSciNetGoogle Scholar
  13. A. Barrat, M. Barthélemy, R. Pastor-Satorras, A. Vespignani, Proc. Natl. Acad. Sci. USA 101, 3747 (2004) CrossRefADSGoogle Scholar
  14. J.J. Ramasco, S.A. Morris, Phys. Rev. E 73, 016122 (2006) CrossRefADSGoogle Scholar
  15. Data available at http://www.nd.edu/ networks/dat-ab-a-s-e/index.html Google Scholar
  16. M.A. Serrano, M. Boguñá, R. Pastor-Satorras, cond-mat/0609029 (2006) Google Scholar
  17. J.J. Ramasco, B. Gonçalves, cond-mat/0609776 (2006) Google Scholar

Copyright information

© EDP Sciences/Società Italiana di Fisica/Springer-Verlag 2007

Authors and Affiliations

  • J. J. Ramasco
    • 1
  1. 1.Physics DepartmentEmory UniversityAtlantaUSA

Personalised recommendations