A Unified Approach to Represent Network Adaptation Principles by Network Reification

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


In this chapter, 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. This is explained for temporal-causal networks where connection weights, combination functions, and speed factors represent the characteristics for Connectivity, Aggregation, and Timing describing the network structure. Having the network structure represented in an explicit manner within the extended network enables to model the adaptation of the base network by dynamics within the reified network: an adaptive network is represented by a non-adaptive network. It is shown how the approach provides a unified modeling perspective on representing network adaptation principles across different domains. This is illustrated for a number of well-known network adaptation principles such as for Hebbian learning in Mental Networks and for network evolution based on homophily in Social Networks.


  1. Banks, D.L., Carley, K.M.: Models for network evolution. J. Math. Sociol. 21, 173–196 (1996)CrossRefGoogle Scholar
  2. Barabasi, A.-L., Albert, R.: Emergence of scaling in random networks. Science 286, 509–512 (1999)MathSciNetCrossRefGoogle Scholar
  3. Bi, G., Poo, M.: Synaptic modification by correlated activity: Hebb’s postulate revisited. Annu. Rev. Neurosci. 24, 139–166 (2001)Google Scholar
  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’16, pp. 1388–1396. IOS Press (2016)Google Scholar
  5. Bowen, K.A., Kowalski, R.: Amalgamating language and meta-language in logic programming. In: Clark, K., Tarnlund, S. (eds.) Logic Programming, pp. 153–172. Academic Press, New York (1982)Google Scholar
  6. Bowen, K.A.: Meta-level programming and knowledge representation. New Gener. Comput. 3, 359–383 (1985)CrossRefGoogle Scholar
  7. Chandra, N., Barkai, E.: A non-synaptic mechanism of complex learning: Modulation of intrinsic neuronal excitability. Neurobiol. Learn. Mem. 154(2018), 30–36 (2018)CrossRefGoogle Scholar
  8. Demers, F.N., Malenfant, J.: Reflection in logic, functional and objectoriented programming: a short comparative study. In: IJCAI’95Workshop on Reflection and Meta-Level Architecture and their Application in AI, pp. 29–38 (1995)Google Scholar
  9. Galton, A.: Operators vs. arguments: the ins and outs of reification. Synthese 150, 415–441 (2006)MathSciNetCrossRefGoogle Scholar
  10. Gerstner, W., Kistler, W.M.: Mathematical formulations of Hebbian learning. Biol. Cybern. 87, 404–415 (2002)Google Scholar
  11. Granovetter, M.S.: The strength of weak ties. Amer. J. Sociol. 78(6), 1360–1380 (1973)CrossRefGoogle Scholar
  12. Hebb, D.: The organisation of behavior. Wiley (1949)Google Scholar
  13. Hofstadter, D.R.: Gödel, Escher, Bach. Basic Books, New York (1979)zbMATHGoogle Scholar
  14. 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
  15. Pearl, J.: Causality. Cambridge University Press (2000)Google Scholar
  16. 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
  17. Smorynski, C.: The incompleteness theorems. In: Barwise, J. (ed.) Handbook of Mathematical Logic, vol. 4, pp. 821–865. North-Holland, Amsterdam (1977)Google Scholar
  18. 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
  19. Sterling, L., Shapiro, E.: The Art of Prolog. MIT Press, (1986) (Ch 17, pp. 319–356)Google Scholar
  20. Sterling, L., Beer, R.: Metainterpreters for expert system construction. J. Logic Program. 6, 163–178 (1989)CrossRefGoogle Scholar
  21. Treur, J.: Network-Oriented Modeling: Addressing Complexity of Cognitive, Affective and Social Interactions. Springer, Berlin (2016)CrossRefGoogle Scholar
  22. Treur, J.: On the applicability of network-oriented modeling based on temporal-causal networks: why network models do not just model networks. J. Inf. Telecommun. 1(1), 23–40 (2017)Google Scholar
  23. Treur, J.: Network reification as a unified approach to represent network adaptation principles within a network. In: Proceedings of the 7th International Conference on Theory and Practice of Natural Computing, TPNC’18. Lecture Notes in Computer Science, vol 11324, pp. 344–358. Springer, Berlin (2018a)Google Scholar
  24. Treur, J.: Multilevel network reification: representing higher order adaptivity in a network. In: Proceedings of the 7th International Conference on Complex Networks and their Applications, ComplexNetworks’18, vol. 1. Studies in Computational Intelligence, vol. 812, 635–651, Springer, Berlin (2018b)Google Scholar
  25. Treur, J.: The ins and outs of network-oriented modeling: from biological networks and mental networks to social networks and beyond. In: LNCS Transactions on Computational Collective Intelligence. Paper on Keynote lecture at the 10th International Conference on Computational Collective Intelligence, ICCCI’18 vol. 32, pp. 120–139 (2019)Google Scholar
  26. Treur, J., Mohammadi Ziabari, S.S.: An adaptive temporal-causal network model for decision making under acute stress. In: Proceedings of the 10th International Conference on Computational Collective Intelligence, ICCCI’18. Lecture Notes in Computer Science, Springer, Berlin (2018)Google Scholar
  27. Weyhrauch, R.W.: Prolegomena to a theory of mechanized formal reasoning. Artif. Intell. 13, 133–170 (1980)MathSciNetCrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

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

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