Modifying Trust Dynamics through Cooperation and Defection in Evolving Social Networks

  • Luca Allodi
  • Luca Chiodi
  • Marco Cremonini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6740)


We present a model of social network that shows a dynamic emergent behavior simulating actors that exchange knowledge based on their preferences, expertise and friendship relations. The network presents a stochastic interaction behavior that tends to create communities, driven by the assortative mixing and triadic closures. Our first research goal is to investigate the features driving the formation of communities and their characteristics under different configurations of the network. In particular we focus on trust which we analyze qualitatively as dependent on the frequency and pattern of interactions. To this aim, we ran simulations of different network configurations and analyzed the resulting statistics. The second research goal is to study the effects of node deception and cooperation on the social network behavior; our primary metric is trust and we evaluated how, under specific conditions, it is possible to manipulate trust in some non trivial ways.


Social Network Personal State Giant Component Emergent Behavior Original Cluster 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Albert, R., Barabasi, A.L.: Statistical mechanics of complex networks. Rev. Mod. Phys. 74(1), 47–97 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Barber, B.: The Logic and Limits of Trust. New Rutgers University Press (1983)Google Scholar
  3. 3.
    Burt, R.S.: Bandwidth and echo: Trust, information, and gossip in social networks. In: Casella, A., Rauch, J.E. (eds.) Networks and Markets: Contributions from Economics and Sociology. Russell Sage Foundation, Thousand Oaks (2001)Google Scholar
  4. 4.
    Golbeck, J.: Trust and nuanced profile similarity in online social networks. ACM Trans. Web, 3:12:1–12:33 (September 2009)Google Scholar
  5. 5.
    Hanaki, N., Peterhansl, A., Dodds, P., Watts, D.: Cooperation in evolving social networks. Management Science 53(7), 1036–1050 (2007)CrossRefzbMATHGoogle Scholar
  6. 6.
    Holme, P., Beom, J.K., Chang, N.Y., Seung, K.H.: Attack vulnerability of complex networks. Physical Review E 65(056109) (2002)Google Scholar
  7. 7.
    Jin, E.M., Girvan, M., Newman, M.E.J.: Structure of growing social networks. Physical Review E 64(4), 046132+ (2001)CrossRefGoogle Scholar
  8. 8.
    Newman, M.E.J.: The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences of the United States of America 98(2), 404–409 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Newman, M.E.J.: Coauthorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Sciences, 5200–5205 (2004)Google Scholar
  10. 10.
    Newman, M.E.J., Girvan, M.: Mixing patterns and community structure in networks. In: Pastor-Satorras, R., Rubi, M., Diaz-Guilera, A. (eds.) Statistical Mechanics of Complex Networks. Lecture Notes in Physics, vol. 625, pp. 66–87. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  11. 11.
    Newman, M., Barabasi, A.L., Watts, D.J.: The Structure and Dynamics of Networks. Princeton University Press, Princeton (2006)zbMATHGoogle Scholar
  12. 12.
    Newman, M.E.J.: The structure and function of complex networks. SIAM Review 45(2), 167–256 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Newman, M.E.J., Park, J.: Why social networks are different from other types of networks. Physical Review E 68(3) (2003)Google Scholar
  14. 14.
    Pastor-Satorras, R., Vazquez, A., Vespignani, A.: Dynamical and correlation properties of the internet. Phis. Rev. Lett. 87(258701) (2001)Google Scholar
  15. 15.
    Santos, F.C., Pacheco, J.M., Lenaerts, T.: Cooperation prevails when individuals adjust their social ties. PLoS Comput. Biol. 2(10), e140 (2006)CrossRefGoogle Scholar
  16. 16.
    Skyrms, B., Pemantle, R.: A dynamic model of social network formation. Proc. Natl. Acad. Sci. U. S. A. 97(16), 9340–9346 (2000)CrossRefzbMATHGoogle Scholar
  17. 17.
    Strogatz, S.H.: Exploring complex networks. Nature 410, 268–276 (2001)CrossRefzbMATHGoogle Scholar
  18. 18.
    Thomborson, C.: Axiomatic and behavioural trust. In: Acquisti, A., Smith, S.W., Sadeghi, A.-R. (eds.) TRUST 2010. LNCS, vol. 6101, pp. 352–366. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  19. 19.
    Tyler, J.R., Wilkinson, D.M., Huberman, B.A.: Email as spectroscopy: automated discovery of community structure within organizations, pp. 81–96. Kluwer, B.V., The Netherlands (2003)Google Scholar
  20. 20.
    Walter, F.E., Battiston, S., Schweitzer, F.: A model of a trust-based recommendation system on a social network. Auton. Agent Multi-Agent Syst. 16(1), 57–74 (2008)CrossRefGoogle Scholar
  21. 21.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393, 440–442 (1998)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Luca Allodi
    • 1
  • Luca Chiodi
    • 1
  • Marco Cremonini
    • 1
  1. 1.University of MilanCrema CRItaly

Personalised recommendations