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The Ins and Outs of Network-Oriented Modeling: From Biological Networks and Mental Networks to Social Networks and Beyond

  • Jan TreurEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11370)

Abstract

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 paper 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. 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, adaptive Mental Network models for Hebbian learning and adaptive Social Network models for evolving relationships. From the more fundamental side, it will be discussed how emerging network behavior can be related to network structure.

References

  1. 1.
    Ashby, W.R.: Design for a Brain, 2nd edn. Wiley, New York (1960)zbMATHGoogle Scholar
  2. 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. 3.
    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
  4. 4.
    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. Theoret. Biol. 214(2002), 105–134 (2002)CrossRefGoogle Scholar
  5. 5.
    Jonker, C.M., Snoep, J.L., Treur, J., Westerhoff, H.V., Wijngaards, W.C.A.: BDI-modelling of complex intracellular dynamics. J. Theoret. Biol. 251, 1–23 (2008)CrossRefGoogle Scholar
  6. 6.
    Kim, J.: Philosophy of Mind. Westview Press, Boulder (1996)Google Scholar
  7. 7.
    Gerstner, W., Kistler, W.M.: Mathematical formulations of Hebbian learning. Biol. Cybern. 87, 404–415 (2002)CrossRefGoogle Scholar
  8. 8.
    Hebb, D.: The Organisation of Behavior. Wiley, Hoboken (1949)Google Scholar
  9. 9.
    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
  10. 10.
    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). http://auai.org/uai2013/prints/papers/24.pdf
  11. 11.
    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). https://www.researchgate.net/profile/Tom_De_jong/publication/30511313_Modelling_of_spatially_complex_human-ecosystem_rural-urban_and_rich-poor_interactions/links/02e7e534d3e9a47836000000.pdf
  12. 12.
    Pearl, J.: Causality. Cambridge University Press, Cambridge (2000)zbMATHGoogle Scholar
  13. 13.
    Port, R.F., van Gelder, T.: Mind as Motion: Explorations in the Dynamics of Cognition. MIT Press, Cambridge (1995)Google Scholar
  14. 14.
    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, vol. 4850, pp. 174–185. Springer-Verlag, Berlin (2007).  https://doi.org/10.1007/978-3-540-77296-5_17CrossRefGoogle Scholar
  15. 15.
    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).  https://doi.org/10.1007/978-1-4757-3554-3CrossRefzbMATHGoogle Scholar
  16. 16.
    Scherer, K.R.: Emotions are emergent processes: they require a dynamic computational architecture. Phil. Trans. R. Soc. B 364, 3459–3474 (2009)CrossRefGoogle Scholar
  17. 17.
    Treur, J.: Verification of temporal-causal network models by mathematical analysis. Vietnam J. Comput. Sci. 3, 207–221 (2016)CrossRefGoogle Scholar
  18. 18.
    Treur, J.: Network-Oriented Modeling: Addressing Complexity of Cognitive, Affective and Social Interactions. Springer, Heidelberg (2016). https://link-springer-com.vu-nl.idm.oclc.org/book/10.1007/978-3-319-45213-5
  19. 19.
    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
  20. 20.
    Uhrmacher, A., Schattenberg, B.: Agents in discrete event simulation. In: Proceedings of the European Symposium on Simulation, ESS 1998, Nottingham, England. Society for Computer Simulation, San Diego (1998)Google Scholar
  21. 21.
    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 (2014)CrossRefGoogle Scholar
  22. 22.
    Westerhoff, H.V., et al.: Macromolecular networks and intelligence in microorganisms. Front. Microbiol. 5, Article 379 (2014)Google Scholar
  23. 23.
    Wright, S.: Correlation and causation. J. Agric. Res. 20, 557–585 (1921)Google Scholar
  24. 24.
    Bosse, T., Duell, R., Memon, Z.A., Treur, J., van der Wal, C.N.: Agent-based modelling of emotion contagion in groups. Cogn. Comput. 7(1), 111–136 (2015)CrossRefGoogle Scholar
  25. 25.
    Treur, J.: Network reification as a unified approach to represent network adaptation principles within a network. In: Proceedings of the 7th International Conference on the Theory and Practice of Natural Computing, TPNC 2018. LNCS. Springer, Heidelberg (2018, to appear)CrossRefGoogle Scholar
  26. 26.
    Treur, J.: Dynamic modeling based on a temporal-causal network modeling approach. Biol. Inspired Cogn. Architect. 16, 131–168 (2016)CrossRefGoogle Scholar
  27. 27.
    Treur, J.: Relating an adaptive network’s structure to its emerging behaviour for Hebbian learning. In: Proceedings of the 7th International Conference on the Theory and Practice of Natural Computing, TPNC 2018. LNCS. Springer, Heidelberg (2018, to appear)CrossRefGoogle Scholar
  28. 28.
    Treur, J.: Relating emerging network behaviour to network structure. In: Proceedings of the 7th International Conference on Complex Networks and Their Applications, Complex Networks 2018. SCI. Springer, Heidelberg (2018, to appear)Google Scholar
  29. 29.
    Treur, J.: Relating an adaptive social network’s structure to its emerging behaviour based on homophily. In: Proceedings of the 7th International Conference on Complex Networks and Their Applications, ComplexNetworks 2018. SCI. Springer, Heidelberg (2018, to appear)Google Scholar
  30. 30.
    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, Complex Networks 2018. SCI. Springer, Heidelberg (2018, to appear)Google Scholar
  31. 31.
    Treur, J.: Mathematical analysis of a network’s asymptotic behaviour based on its strongly connected components. In: Proceedings of the 7th International Conference on Complex Networks and Their Applications, Complex Networks 2018. SCI. Springer, Heidelberg (2018, to appear)Google Scholar
  32. 32.
    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 2009, pp. 243–252. ACM Press (2009)Google Scholar
  33. 33.
    Harary, F., Norman, R.Z., Cartwright, D.: Structural Models: an Introduction to the Theory of Directed Graphs. Wiley, New York (1965)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Behavioural Informatics GroupVrije Universiteit AmsterdamAmsterdamNetherlands

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