International Conference on Analysis of Images, Social Networks and Texts

Analysis of Images, Social Networks and Texts pp 263-274 | Cite as

Formation and Evolution Mechanisms in Online Network of Students: The Vkontakte Case

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 542)

Abstract

The mechanisms of real-world social network formation and evolution are one of the most important topics in the field of network science. In this study we collect data about the development of the Vkontakte (a popular Russian social networking site) network of first-year students at a Russian university. We analyze the network formation process from the moment of network establishing until its stabilization. Using Conditional Uniform Graph Test, we compare the graph-level indices of the observed network with random same-size networks that were generated according to random, preferential attachment, and small-world algorithms. We propose two explanatory mechanisms of online network growth: the connected component attachment mechanism and the brokerage mechanism.

Keywords

Network growth Network evolution Online networks Student networks Higher education 

References

  1. 1.
    Subrahmanyam, K., Reich, S.M., Waechter, N., Espinoza, G.: Online and offline social networks: use of social networking sites by emerging adults. J. Appl. Dev. Psychol. 29, 420–433 (2008)CrossRefGoogle Scholar
  2. 2.
    Calvó-Armengol, A., Patacchini, E., Zenou, Y.: Peer effects and social networks in education. Rev. Econ. Stud. 76, 1239–1267 (2009)MathSciNetCrossRefMATHGoogle Scholar
  3. 3.
    Conti, G., Galeotti, A., Mueller, G., Pudney, S.: Popularity. J. Hum. Resour. 48, 1072–1094 (2013)Google Scholar
  4. 4.
    Fletcher, J.: Friends or family? revisiting the effects of high school popularity on adult earnings. Appl. Econ. 46, 2408–2417 (2014)CrossRefGoogle Scholar
  5. 5.
    Backstrom, L., Huttenlocher, D., Kleinberg, J., Lan, X.: Group formation in large social networks: membership, growth, and evolution. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 44–54. ACM (2006)Google Scholar
  6. 6.
    Kairam, S.R., Wang, D.J., Leskovec, J.: The life and death of online groups: predicting group growth and longevity. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, pp. 673–682. ACM (2012)Google Scholar
  7. 7.
    Leskovec, J., Backstrom, L., Kumar, R., Tomkins, A.: Microscopic evolution of social networks. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 462–470. ACM (2008)Google Scholar
  8. 8.
    Capocci, A., Servedio, V.D., Colaiori, F., Buriol, L.S., Donato, D., Leonardi, S., Caldarelli, G.: Preferential attachment in the growth of social networks: the internet encyclopedia wikipedia. Phys. Rev. E 74, 036116 (2006)CrossRefGoogle Scholar
  9. 9.
    Mislove, A., Koppula, H.S., Gummadi, K.P., Druschel, P., Bhattacharjee, B.: Growth of the flickr social network. In: Proceedings of the First Workshop on Online Social Networks, pp. 25–30. ACM (2008)Google Scholar
  10. 10.
    Garg, S., Gupta, T., Carlsson, N., Mahanti, A.: Evolution of an online social aggregation network: an empirical study. In: Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement Conference, pp. 315–321. ACM (2009)Google Scholar
  11. 11.
    Hu, H., Wang, X.: Evolution of a large online social network. Phys. Lett. A 373, 1105–1110 (2009)CrossRefGoogle Scholar
  12. 12.
    Borge-Holthoefer, J., Baños, R.A., González-Bailón, S., Moreno, Y.: Cascading behaviour in complex socio-technical networks. J. Complex Netw. 1, 3–24 (2013)CrossRefGoogle Scholar
  13. 13.
    Bakshy, E., Rosenn, I., Marlow, C., Adamic, L.: The role of social networks in information diffusion. In: Proceedings of the 21st International Conference on World Wide Web, pp. 519–528. ACM (2012)Google Scholar
  14. 14.
    Lewis, K., Gonzalez, M., Kaufman, J.: Social selection and peer influence in an online social network. Proc. Natl. Acad. Sci. 109, 68–72 (2012)CrossRefGoogle Scholar
  15. 15.
    Erdos, P., Rényi, A.: On the evolution of random graphs. Publ. Math. Inst. Hung. Acadamy Sci. 38, 343–347 (1961)MathSciNetMATHGoogle Scholar
  16. 16.
    Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286, 509–512 (1999)MathSciNetCrossRefMATHGoogle Scholar
  17. 17.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of “small-world” networks. Nature 393, 440–442 (1998)CrossRefGoogle Scholar
  18. 18.
    Csardi, G., Nepusz, T.: The igraph software package for complex network research. Inter. J. Complex Syst. 1695, 1–9 (2006)Google Scholar
  19. 19.
    Statistical Package, R.: R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2009)Google Scholar
  20. 20.
    Snijders, T.A., Van de Bunt, G.G., Steglich, C.E.: Introduction to stochastic actor-based models for network dynamics. Soc. Netw. 32, 44–60 (2010)CrossRefGoogle Scholar
  21. 21.
    Krivitsky, P.N., Handcock, M.S.: A separable model for dynamic networks. J. R. Stat. Soc. Ser. B (Statistical Methodology) 76, 29–46 (2014)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Anderson, B.S., Butts, C., Carley, K.: The interaction of size and density with graph-level indices. Soc. Netw. 21, 239–267 (1999)CrossRefGoogle Scholar
  23. 23.
    Goodreau, S.M., Kitts, J.A., Morris, M.: Birds of a feather, or friend of a friend? using exponential random graph models to investigate adolescent social networks. Demography 46, 103–125 (2009)CrossRefGoogle Scholar
  24. 24.
    Vaquero, L.M., Cebrian, M.: The rich club phenomenon in the classroom. Scientific reports 3 (2013)Google Scholar
  25. 25.
    Burt, R.S.: Structural holes and good ideas. Am. J. Sociol. 110, 349–399 (2004)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sofia Dokuka
    • 1
  • Diliara Valeeva
    • 1
  • Maria Yudkevich
    • 1
  1. 1.Center for Institutional StudiesNRU HSEMoscowRussia

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