Ins and Outs of Network-Oriented Modeling

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


Network-Oriented Modeling has successfully been applied to obtain network models for a wide range of phenomena, including Biological Networks, Mental Networks, and Social Networks. In this chapter, it is discussed how the interpretation of a network as a causal network and taking into account dynamics in the form of temporal-causal networks, brings more depth. Thus main characteristics for a network structure are obtained: Connectivity in terms of the connections and their weights, Aggregation of multiple incoming connections in terms of combination functions, and Timing in terms of speed factors. The basics and the scope of applicability of such a Network-Oriented Modelling approach are discussed and illustrated. This covers, for example, Social Network models for social contagion or information diffusion, and Mental Network models for cognitive and affective processes. From the more fundamental side, it will be discussed how emerging network behavior can be related to network structure.


Network-Oriented Modeling Temporal-causal network 


  1. Ashby, W.R.: Design for a Brain, 2nd edn. Wiley, New York (1960)zbMATHGoogle Scholar
  2. Bell, A.: Levels and loops: the future of artificial intelligence and neuroscience. Phil. Trans. R. Soc. Lond. B 354, 2013–2020 (1999)CrossRefGoogle Scholar
  3. Chen, Y.: General spanning trees and reachability query evaluation. In: Desai, B.C. (ed.), Proceedings of the 2nd Canadian Conference on Computer Science and Software Engineering, C3S2E’09, pp. 243–252. ACM Press (2009)Google Scholar
  4. Gerstner, W., Kistler, W.M.: Mathematical formulations of Hebbian learning. Biol. Cybern. 87, 404–415 (2002)CrossRefGoogle Scholar
  5. Harary, F., Norman, R.Z., Cartwright, D.: Structural Models: An Introduction to the Theory of Directed Graphs. Wiley, New York (1965)zbMATHGoogle Scholar
  6. Hebb, D.: The Organisation of Behavior. Wiley, New York (1949)Google Scholar
  7. Jonker, C.M., Snoep, J.L., Treur, J., Westerhoff, H.V., Wijngaards, W.C.A.: Putting intentions into cell biochemistry: an artificial intelligence perspective. J. Theor. Biol. 214(2002), 105–134 (2002)CrossRefGoogle Scholar
  8. Jonker, C.M., Snoep, J.L., Treur, J., Westerhoff, H.V., Wijngaards, W.C.A.: BDI-modelling of complex intracellular dynamics. J. Theor. Biol. 251, 1–23 (2008)CrossRefGoogle Scholar
  9. Kim, J.: Philosophy of Mind. Westview Press (1996)Google Scholar
  10. Lorenz, E.N.: Deterministic nonperiodic flow. J. Atmos. Sci. 20(2), 130–141 (1963)CrossRefGoogle Scholar
  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. Mooij, J.M., Janzing, D., Schölkopf, B.: From differential equations to structural causal models: the deterministic case. In: Nicholson, A., Smyth, P. (eds.), Proceedings of the 29th Annual Conference on Uncertainty in Artificial Intelligence (UAI-13), pp. 440–448. AUAI Press (2013). URL:
  13. Naudé, A., Le Maitre, D., de Jong, T., Mans, G.F.G., Hugo, W.: Modelling of spatially complex human-ecosystem, rural-urban and rich-poor interactions (2008). URL:
  14. Pearl, J.: Causality. Cambridge University Press, Cambridge (2000)Google Scholar
  15. Port, R.F., van Gelder, T.: Mind as motion: explorations in the dynamics of cognition. MIT Press, Cambridge, MA (1995)Google Scholar
  16. Potter, S.M.: What can artificial intelligence get from neuroscience? In: Lungarella, M., Bongard, J., Pfeifer, R. (eds.) Artificial Intelligence Festschrift: The next 50 years. Springer, Berlin (2007)Google Scholar
  17. Sarjoughian, H., Cellier, F.E. (eds.): Discrete Event Modeling and Simulation Technologies: A Tapestry of Systems and AI-Based Theories and Methodologies. Springer, Berlin (2001)zbMATHGoogle Scholar
  18. Scherer, K.R.: Emotions are emergent processes: they require a dynamic computational architecture. Phil. Trans. R. Soc. B 364, 3459–3474 (2009)CrossRefGoogle Scholar
  19. Treur, J.: Network-Oriented Modeling: Addressing Complexity of Cognitive, Affective and Social Interactions. Springer Publishers, Berlin (2016) Downloadable at URL: Scholar
  20. 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, 23–40 (2017)Google Scholar
  21. Treur, J.: The ins and outs of network-oriented modeling: from biological networks and mental networks to social networks and beyond. In: Transactions on Computational Collective Intelligence vol. 32, pp. 120–139. Springer Publishers, Berlin. Contents of Keynote Lecture at ICCCI’18 (2019)Google Scholar
  22. Uhrmacher, A., Schattenberg, B.: Agents in discrete event simulation. In: Proceedings of the European Symposium on Simulation (ESS ’98, Nottingham, England). Society for Computer Simulation, San Diego, CA (1998)Google Scholar
  23. Westerhoff, H.V., He, F., Murabito, E., Crémazy, F., Barberis, M.: Understanding principles of the dynamic biochemical networks of life through systems biology. In: Kriete, A., Eils, R. (eds.) Computational Systems Biology, 2nd edn, pp. 21–44. Academic Press, Oxford (2014a)CrossRefGoogle Scholar
  24. Westerhoff, H.V., Brooks, A.N., Simeonidis, E., García-Contreras, R., He, F., Boogerd, F.C., Jackson, V.J., Goncharuk, V., Kolodkin, A.: Macromolecular networks and intelligence in microorganisms. Front. Microbiol. 5, Article 379 (2014b)Google Scholar
  25. Wright, S.: Correlation and causation. J. Agric. Res. 20, 557–585 (1921)Google 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