Using Multilevel Network Reification to Model Second-Order Adaptive Bonding by Homophily

  • Jan TreurEmail author
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 251)


The concept of multilevel network reification introduced in the previous chapters enables representation within a network not only of first-order adaptation principles, but also of second-order adaptation principles expressing change of characteristics of first-order adaptation principles. In the current chapter, this approach is illustrated for an adaptive Social Network. This involves a first-order adaptation principle for bonding by homophily represented at the first reification level, and a second-order adaptation principle describing change of characteristics of this first-order adaptation principle, and represented at the second reification level. The second-order adaptation addresses adaptive change of two of the characteristics of the first-order adaptation, specifically similarity tipping point and connection adaptation speed factor.


  1. Blankendaal, R., Parinussa, S., Treur, J.: A temporal-causal modelling approach to integrated contagion and network change in social networks. In: Proceeding of the 22nd European Conference on Artificial Intelligence, ECAI’16. IOS Press, Frontiers in Artificial Intelligence and Applications, vol. 285, pp. 1388–1396 (2016)Google Scholar
  2. Boomgaard, G., Lavitt, F., Treur, J.: Computational analysis of social contagion and homophily based on an adaptive social network model. In: Proceedings of the 10th International Conference on Social Informatics, SocInfo’18. Lecture Notes in Computer Science, vol. 11185, pp. 86–101, Springer Publishers (2018)Google Scholar
  3. Byrne, D.: The attraction hypothesis: do similar attitudes affect anything? J. Pers. Soc. Psychol. 51(6), 1167–1170 (1986)CrossRefGoogle Scholar
  4. Carley, K.M.: Inhibiting adaptation. In: Proceedings of the 2002 Command and Control Research and Technology Symposium, pp. 1–10. Naval Postgraduate School, Monterey, CA (2002)Google Scholar
  5. Carley, K.M.: Destabilization of covert networks. Comput. Math. Organ. Theor. 12, 51–66 (2006)CrossRefGoogle Scholar
  6. Carley, K.M., Lee, J.-S., Krackhardt, D.: Destabilizing networks. Connections 24(3), 31–34 (2001)Google Scholar
  7. Holme, P., Newman, M.E.J.: Nonequilibrium phase transition in the coevolution of networks and opinions. Phys. Rev. E 74(5), 056108 (2006)CrossRefGoogle Scholar
  8. Kozyreva, O., Pechina, A. Treur, J.: Network-oriented modeling of multi-criteria homophily and opinion dynamics in social media. In: Koltsova, O., Ignatov, D.I., Staab, S. (eds.) Social Informatics: Proceedings of the 10th International Conference on Social Informatics, SocInfo’18, vol. 1. Lecture Notes in AI, vol. 11185, pp. 322–335 Springer (2018)Google Scholar
  9. Levy, D.A., Nail, P.R.: Contagion: a theoretical and empirical review and reconceptualization. Genet. Soc. Gen. Psychol. Monogr. 119(2), 233–284 (1993)Google Scholar
  10. McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: homophily in social networks. Annu. Rev. Soc. 27, 415–444 (2001)CrossRefGoogle Scholar
  11. Pearson, M., Steglich, C., Snijders, T.: Homophily and assimilation among sport-active adolescent substance users. Connections 27(1), 47–63 (2006)Google Scholar
  12. Sharpanskykh, A., Treur, J.: Modelling and analysis of social contagion in dynamic networks. Neurocomputing 146, 140–150 (2014)CrossRefGoogle Scholar
  13. Treur, J.: Network-oriented modeling: addressing complexity of cognitive, affective and social interactions. Springer Publishers (2016)Google Scholar
  14. van Beukel, S., Goos, S., Treur, J.: An adaptive temporal-causal network model for social networks based on the homophily and more-becomes-more principle. Neurocomputing 338, 361–371 (2019)CrossRefGoogle Scholar
  15. Vazquez, F.: Opinion dynamics on coevolving networks. In: Mukherjee, A., Choudhury, M., Peruani, F., Ganguly, N., Mitra, B. (eds.) Dynamics On and Of Complex Networks, Volume 2, Modeling and Simulation in Science, Engineering and Technology, pp. 89–107. Springer, New York (2013)Google Scholar
  16. Vazquez, F., Gonzalez-Avella, J.C., Eguíluz, V.M., San Miguel, M.: Time-scale competition leading to fragmentation and recombination transitions in the coevolution of network and states. Phys. Rev. E 76, 046120 (2007)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Social AI Group, Department of Computer ScienceVrije Universiteit AmsterdamAmsterdamThe Netherlands

Personalised recommendations