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

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Theory and Practice of Natural Computing (TPNC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11324))

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Abstract

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.

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References

  1. Banks, D.L., Carley, K.M.: Models for network evolution. J. Math. Sociol. 21, 173–196 (1996)

    Article  Google Scholar 

  2. Barabasi, A.L., Albert, R.: Emergence of scaling in random networks. Science 286, 509–512 (1999)

    Article  MathSciNet  Google Scholar 

  3. Bi, G., Poo, M.: Synaptic modification by correlated activity: Hebb’s postulate revisited. Annu. Rev. Neurosci. 24, 139–166 (2001)

    Article  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 2016, pp. 1388–1396. IOS Press (2016)

    Google Scholar 

  5. Bowen, K.A.: Meta-level programming and knowledge representation. New Gener. Comput. 3, 359–383 (1985)

    Article  Google Scholar 

  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. 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. Galton, A.: Operators vs. arguments: the ins and outs of reification. Synthese 150, 415–441 (2006)

    Article  MathSciNet  Google Scholar 

  9. Gerstner, W., Kistler, W.M.: Mathematical formulations of Hebbian learning. Biol. Cybern. 87, 404–415 (2002)

    Article  Google Scholar 

  10. Hebb, D.O.: The organization of behavior: a neuropsychological theory (1949)

    Google 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)

    Article  Google Scholar 

  12. Pearl, J.: Causality. Cambridge University Press, New York (2000)

    MATH  Google Scholar 

  13. Rapoport, A.: Spread of Information through a Population with Socio-structural Bias: I. Assumption of transitivity. Bull. Math. Biophys. 15, 523–533 (1953)

    Article  MathSciNet  Google Scholar 

  14. Smorynski, C.: The incompleteness theorems. In: Barwise, J. (ed.) Handbook of Mathematical Logic, North-Holland, Amsterdam, vol. 4, pp. 821–865 (1977)

    Google Scholar 

  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)

    Article  Google Scholar 

  16. Sterling, L., Shapiro, E.: The Art of Prolog. MIT Press, Ch 17, pp. 319–356 (1986)

    Google Scholar 

  17. Sterling, L., Beer, R.: Metainterpreters for expert system construction. J. Logic Program. 6, 163–178 (1989)

    Article  Google Scholar 

  18. Treur, J.: Network-Oriented Modeling: Addressing Complexity of Cognitive, Affective and Social Interactions. Springer, Cham (2016)

    Book  Google Scholar 

  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. 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. 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). https://doi.org/10.1007/978-3-319-98446-9_2

    Chapter  Google Scholar 

  22. Weyhrauch, R.W.: Prolegomena to a theory of mechanized formal reasoning. Artif. Intell. 13, 133–170 (1980)

    Article  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Behavioural Informatics Group, Vrije Universiteit Amsterdam, Amsterdam, Netherlands

    Jan Treur

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  1. Jan Treur
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Correspondence to Jan Treur .

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Editors and Affiliations

  1. School of Business, University College Dublin, Dublin, Ireland

    David Fagan

  2. Rovira i Virgili University, Tarragona, Spain

    Carlos Martín-Vide

  3. School of Business, University College Dublin, Dublin, Ireland

    Michael O'Neill

  4. Escuela Politécnica, University of Extremadura, Caceres, Spain

    Miguel A. Vega-Rodríguez

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Treur, J. (2018). Network Reification as a Unified Approach to Represent Network Adaptation Principles Within a Network. In: Fagan, D., Martín-Vide, C., O'Neill, M., Vega-Rodríguez, M.A. (eds) Theory and Practice of Natural Computing. TPNC 2018. Lecture Notes in Computer Science(), vol 11324. Springer, Cham. https://doi.org/10.1007/978-3-030-04070-3_27

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  • DOI: https://doi.org/10.1007/978-3-030-04070-3_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04069-7

  • Online ISBN: 978-3-030-04070-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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