Network Reification as a Unified Approach to Represent Network Adaptation Principles Within a Network

  • Jan TreurEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11324)


In this paper the notion of network reification is introduced: a construction by which a given (base) network is extended by adding explicit states representing the characteristics defining the base network’s structure. Having the network structure represented in an explicit manner within the extended network enhances expressiveness and enables to model adaptation of the base network by dynamics within the reified network. It is shown how the approach provides a unified modeling perspective on representing network adaptation principles across different domains. This is illustrated by a number of known network adaptation principles such as for Hebbian learning in Mental Networks and for network evolution based on homophily in Social Networks.


Network reification Adaptation principle 


  1. 1.
    Banks, D.L., Carley, K.M.: Models for network evolution. J. Math. Sociol. 21, 173–196 (1996)CrossRefGoogle Scholar
  2. 2.
    Barabasi, A.L., Albert, R.: Emergence of scaling in random networks. Science 286, 509–512 (1999)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Bi, G., Poo, M.: Synaptic modification by correlated activity: Hebb’s postulate revisited. Annu. Rev. Neurosci. 24, 139–166 (2001)CrossRefGoogle Scholar
  4. 4.
    Blankendaal, R., Parinussa, S., Treur, J.: A temporal-causal modelling approach to integrated contagion and network change in social networks. In: Proceedings of the 22nd European Conference on Artificial Intelligence, ECAI 2016, pp. 1388–1396. IOS Press (2016)Google Scholar
  5. 5.
    Bowen, K.A.: Meta-level programming and knowledge representation. New Gener. Comput. 3, 359–383 (1985)CrossRefGoogle Scholar
  6. 6.
    Bowen, K.A., Kowalski, R.: Amalgamating language and meta-language in logic programming. In: Logic Programming, pp. 153–172. Academic Press, New York (1982)Google Scholar
  7. 7.
    Demers, F.N., Malenfant, J.: Reflection in logic, functional and objectoriented programming: a short comparative study. In: IJCAI 1995 Workshop on Reflection and Meta-Level Architecture and Their Application in AI, pp. 29–38 (1995)Google Scholar
  8. 8.
    Galton, A.: Operators vs. arguments: the ins and outs of reification. Synthese 150, 415–441 (2006)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Gerstner, W., Kistler, W.M.: Mathematical formulations of Hebbian learning. Biol. Cybern. 87, 404–415 (2002)CrossRefGoogle Scholar
  10. 10.
    Hebb, D.O.: The organization of behavior: a neuropsychological theory (1949)Google Scholar
  11. 11.
    McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: homophily in social networks. Annu. Rev. Sociol. 27, 415–444 (2001)CrossRefGoogle Scholar
  12. 12.
    Pearl, J.: Causality. Cambridge University Press, New York (2000)zbMATHGoogle Scholar
  13. 13.
    Rapoport, A.: Spread of Information through a Population with Socio-structural Bias: I. Assumption of transitivity. Bull. Math. Biophys. 15, 523–533 (1953)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Smorynski, C.: The incompleteness theorems. In: Barwise, J. (ed.) Handbook of Mathematical Logic, North-Holland, Amsterdam, vol. 4, pp. 821–865 (1977)CrossRefGoogle Scholar
  15. 15.
    Sousa, N., Almeida, O.F.X.: Disconnection and reconnection: the morphological basis of (mal)adaptation to stress. Trends Neurosci. 35(12), 742–751 (2012)CrossRefGoogle Scholar
  16. 16.
    Sterling, L., Shapiro, E.: The Art of Prolog. MIT Press, Ch 17, pp. 319–356 (1986)Google Scholar
  17. 17.
    Sterling, L., Beer, R.: Metainterpreters for expert system construction. J. Logic Program. 6, 163–178 (1989)CrossRefGoogle Scholar
  18. 18.
    Treur, J.: Network-Oriented Modeling: Addressing Complexity of Cognitive, Affective and Social Interactions. Springer, Cham (2016)CrossRefGoogle Scholar
  19. 19.
    Treur, J.: On the applicability of network-oriented modeling based on temporal-causal networks. J. Inf. Telecommun. 1(1), 23–40 (2017)Google Scholar
  20. 20.
    Treur, J.: The Ins and Outs of Network-Oriented Modeling: From Biological Networks and Mental Networks to Social Networks and Beyond. Transactions on Computational Collective Intelligence, Springer Publishers. Paper for Keynote lecture at the 10th International Conference on Computational Collective Intelligence, ICCCI 2018 (2018)Google Scholar
  21. 21.
    Treur, J., Mohammadi Ziabari, S.S.: An adaptive temporal-causal network model for decision making under acute stress. In: Nguyen, N.T., Pimenidis, E., Khan, Z., Trawiński, B. (eds.) ICCCI 2018. LNCS (LNAI), vol. 11056, pp. 13–25. Springer, Cham (2018). Scholar
  22. 22.
    Weyhrauch, R.W.: Prolegomena to a theory of mechanized formal reasoning. Artif. Intell. 13, 133–170 (1980)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Behavioural Informatics GroupVrije Universiteit AmsterdamAmsterdamNetherlands

Personalised recommendations