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Network-Oriented Modeling for Adaptive Networks: Designing Higher-Order Adaptive Biological, Mental and Social Network Models pp 321–352Cite as

Relating a Reified Adaptive Network’s Structure to Its Emerging Behaviour for Bonding by Homophily

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Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 251))

Abstract

In this chapter it is analysed how emerging behaviour in an adaptive social network for bonding can be related to characteristics of the adaptive network’s structure, which includes the structure of the adaptation principles incorporated. In particular, this is addressed for adaptive social networks for bonding based on homophily and for community formation in such adaptive social networks. To this end, relevant characteristics of the reified network structure (including the adaptation principle) have been identified, such as a tipping point for similarity as used for homophily. Applying network reification, the adaptive network characteristics are represented by reification states in the extended network, and adaptation principles are described by characteristics of these reification states, in particular their connectivity characteristics (their connections) and their aggregation characteristics (in terms of their combination functions). According to this network reification approach, as one of the results it has been found how the emergence of communities strongly depends on the value of this similarity tipping point. Moreover, it is shown that some characteristics entail that the connection weights all converge to 0 (for persons in different communities) or 1 (for persons within one community).

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References

  • Ashby, W.R.: Design for a Brain. Chapman and Hall, London (second extended edition) (1960) (First edition, 1952)

    Google Scholar 

  • Axelrod, R.: The dissemination of culture: a model with local convergence and global polarization. J. Conflict Resolut. 41(2), 203–226 (1997)

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  • 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. Frontiers in Artificial Intelligence and Applications, vol. 285, pp. 1388–1396. IOS Press (2016)

    Google Scholar 

  • Bloem, R., Gabow, H.N., Somenzi, F.: An algorithm for strongly connected component analysis in n log n symbolic steps. Formal Method Syst. Des. 28, 37–56 (2006)

    Article  Google Scholar 

  • Boomgaard, G., Lavitt, F., Treur, J.: Computational analysis of social contagion and homophily based on an adaptive social network model. In: Proceedings of the 10th International Conference on Social Informatics, SocInfo’18. Lecture Notes in Computer Science, vol. 11185, pp. 86–101. Springer Publishers (2018)

    Google Scholar 

  • Bornholdt, S., Ebel, H.: World Wide Webscaling exponent from Simon’s 1955 model. Phys. Rev. E 64, art. no. 035104 (2001)

    Google Scholar 

  • Brauer, F., Nohel, J.A.: Qualitative Theory of Ordinary Differential Equations. Benjamin (1969)

    Google Scholar 

  • de Solla Price, D.J.: A general theory of bibliometric and other cumulative advantage processes. J. Am. Soc. Inform. Sci. 27, 292–306 (1976)

    Article  Google Scholar 

  • Fleischer, L.K., Hendrickson, B., Pınar, A.: On identifying strongly connected components in parallel. In: Rolim, J. (ed.) Parallel and Distributed Processing. IPDPS 2000. Lecture Notes in Computer Science, vol. 1800, pp. 505–511. Springer (2000)

    Google Scholar 

  • Gentilini, R., Piazza, C., Policriti, A.: Computing strongly connected components in a linear number of symbolic steps. In: Proceedings of the SODA’03, pp. 573–582 (2003)

    Google Scholar 

  • Glasgow Empirical Data: https://www.stats.ox.ac.uk/~snijders/siena/Glasgow_data.htm (2016)

  • Gross, T., Sayama, H. (eds.): Adaptive Networks: Theory, Models and Applications. Springer (2009)

    Google Scholar 

  • Harary, F., Norman, R.Z., Cartwright, D.: Structural Models: An Introduction to the Theory of Directed Graphs. Wiley, New York (1965)

    MATH  Google Scholar 

  • Heijmans, P., van Stijn, J., Treur, J.: Modeling cultural segregation of the queer community through an adaptive social network model. In: Proceedings of the Fourth International Congress on Information and Communication Technology, ICICT’19. Advances in Intelligent Systems and Computing. Springer (2019)

    Google Scholar 

  • Hirsch, M.W.: The dynamical systems approach to differential equations. Bull. (New Ser.) Am. Math. Soc. 11, 1–64 (1984)

    Google Scholar 

  • Holme, P., Newman, M.E.J.: Nonequilibrium phase transition in the coevolution of networks and opinions. Phys. Rev. E 74(5), 056108 (2006)

    Article  Google Scholar 

  • Kappert, C., Rus, R., Treur, J.: On the emergence of segregation in society: network-oriented analysis of the effect of evolving friendships. In: Nguyen, N.T., Pimenidis, E., Khan, Z., Trawinski, B. (eds.) Computational Collective Intelligence: 10th International Conference, ICCCI 2018, Proceedings, vol. 1. Lecture Notes in Artificial Intelligence, vol. 11055, pp. 178–191. Springer (2018)

    Google Scholar 

  • Knecht, A.: Empirical data: collected by Andrea Knecht (2008). https://www.stats.ox.ac.uk/*snijders/siena/tutorial2010_data.htm

  • Kozyreva, O., Pechina, A., Treur, J.: Network-oriented modeling of multi-criteria homophily and opinion dynamics in social media. In: Koltsova, O., Ignatov, D.I., Staab, S. (eds.) Social Informatics: Proceedings of the 10th International Conference on Social Informatics, SocInfo’18, vol. 1. Lecture Notes in AI, vol. 11185, pp. 322–335. Springer (2018)

    Google Scholar 

  • Kuich, W.: On the Entropy of Context-Free Languages. Information and Control 16, 173–200 (1970)

    Article  MathSciNet  Google Scholar 

  • Kuipers, B.J.: Commonsense reasoning about causality: deriving behavior from structure. Artif. Intell. 24, 169–203 (1984)

    Article  Google Scholar 

  • Kuipers, B.J., Kassirer, J.P.: How to discover a knowledge representation for causal reasoning by studying an expert physician. In: Proceedings of the Eighth International Joint Conference on Artificial Intelligence, IJCAI’83. William Kaufman, Los Altos, CA (1983)

    Google Scholar 

  • Łacki, J.: Improved deterministic algorithms for decremental reachability and strongly connected components. ACM Trans. Algorithms 9(3), Article 27 (2013)

    Google Scholar 

  • Levy, D.A., Nail, P.R.: Contagion: a theoretical and empirical review and reconceptualization. Genet. Soc. Gen. Psychol. Monogr. 119(2), 233–284 (1993)

    Google Scholar 

  • Li, G., Zhu, Z., Cong, Z., Yang, F.: Efficient decomposition of strongly connected components on GPUs. J. Syst. Architect. 60(1), 1–10 (2014)

    Article  Google Scholar 

  • Lotka, A.J.: Elements of Physical Biology. Williams and Wilkins Co. (1924). Dover Publications, 2nd edn (1956)

    Google Scholar 

  • McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: homophily in social networks. Ann. Rev. Sociol. 27(1), 415–444 (2001)

    Article  Google Scholar 

  • Merton, R.K.: The Matthew effect in science. Science 159(1968), 56–63 (1968)

    Article  Google Scholar 

  • Newman, M.E.J.: The structure and function of complex networks. SIAM Rev. 45, 167–256 (2003)

    Article  MathSciNet  Google Scholar 

  • Pearl, J.: Causality. Cambridge University Press (2000)

    Google Scholar 

  • Pearson, M., Steglich, C., Snijders, T.: Homophily and assimilation among sport-active adolescent substance users. Connections 27(1), 47–63 (2006)

    Google Scholar 

  • Roller, R., Blommestijn, S.Q., Treur, J.: An adaptive computational network model for multi-emotional social interaction. In: Proceedings of the 6th International Conference on Complex Networks and their Applications, COMPLEXNETWORKS’17. Studies in Computational Intelligence. Springer (2017)

    Google Scholar 

  • Sharpanskykh, A., Treur, J.: Modelling and analysis of social contagion processes with dynamic networks. In: Bǎdicǎ, C., Nguyen, N.T., Brezovan, M. (eds.) Computational Collective Intelligence. Technologies and Applications. ICCCI 2013. Lecture Notes in Computer Science, vol. 8083, pp. 40–50. Springer, Berlin (2013)

    Google Scholar 

  • Sharpanskykh, A., Treur, J.: Modelling and analysis of social contagion in dynamic networks. Neurocomputing 146(2014), 140–150 (2014)

    Article  Google Scholar 

  • Simon, H.A.: On a class of skew distribution functions. Biometrika 42(1955), 425–440 (1955)

    Article  MathSciNet  Google Scholar 

  • Tarjan, R.: Depth-first search and linear graph algorithms. SIAM J. Comput. 1(2), 146–160 (1972)

    Article  MathSciNet  Google Scholar 

  • Treur, J.: Verification of temporal-causal network models by mathematical analysis. Vietnam J. Comput. Sci. 3, 207–221 (2016a)

    Article  Google Scholar 

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

    Google Scholar 

  • Treur, J.: Relating emerging network behaviour to network structure. In: Aiello, L.M., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L.M. (eds.) Proceedings of the 7th International Conference on Complex Networks and their Applications, ComplexNetworks’18. Studies in Computational Intelligence, vol. 812, pp. 619–634. Springer Publishers (2018a)

    Google Scholar 

  • Treur, J.: Relating an adaptive social network’s structure to its emerging behaviour based on homophily. In: Aiello, L.M., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L.M. (eds.) Proceedings of the 7th International Conference on Complex Networks and their Applications, Complex Networks’18. Studies in Computational Intelligence, vol. 812, pp. 635–651. Springer Publishers (2018b)

    Google Scholar 

  • Treur, J.: Mathematical analysis of a network’s asymptotic behaviour based on its strongly connected components. In: Proc. of the 7th International Conference on Complex Networks and their Applications, ComplexNetworks’18, vol. 1. Studies in Computational Intelligence, vol. 812, pp. 663–679. Springer Publishers (2018c)

    Google Scholar 

  • 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, pp. 635–651. Springer (2018d)

    Google Scholar 

  • Treur, J.: The ins and outs of network-oriented modeling: from biological networks and mental networks to social networks and beyond. Trans. Comput. Collect. Intell. 32, 120–139 (2019). Text of Keynote Lecture at the 10th International Conference on Computational Collective Intelligence, ICCCI’18 (2018)

    Google Scholar 

  • Turnbull, L., Hütt, M.-T., Ioannides, A.A., Kininmonth, S., Poeppl, R., Tockner, K., Bracken, L.J., Keesstra, S., Liu, L., Masselink, R., Parsons, A.J.: Connectivity and complex systems: Learning from a multi-disciplinary perspective. Appl. Netw. Sci. 3(47) (2018). https://doi.org/10.1007/s41109-018-0067-2

  • van Beukel, S., Goos, S., Treur, J.: Understanding homophily and more-becomes-more through adaptive temporal-causal network models. In: De la Prieta, F. (ed.) Trends in Cyber-Physical Multi-Agent Systems. The PAAMS Collection—15th International Conference PAAMS’17. Advances in Intelligent Systems and Computing, vol. 619, pp. 16–29. Springer (2017)

    Google Scholar 

  • van Beukel, S., Goos, S., Treur, J.: An adaptive temporal-causal network model for social networks based on the homophily and more-becomes-more principle. Neurocomputing 338, 361–371 (2019)

    Article  Google Scholar 

  • van Dijk, M., Treur, J.: Physical activity contagion and homophily in an adaptive social network model. In: Nguyen, N.T., Pimenidis, E., Khan, Z., Trawinski, B. (eds.) Computational Collective Intelligence: 10th International Conference, ICCCI 2018, Proceedings. vol. 1. Lecture Notes in AI, vol. 11055, pp. 87–98. Springer (2018)

    Google Scholar 

  • van Gerwen, S., van Meurs, A., Treur, J.: An adaptive temporal-causal network for representing changing opinions on music releases. In: De La Prieta F., Omatu S., Fernández-Caballero, A. (eds.) Distributed Computing and Artificial Intelligence, 15th International Conference. DCAI 2018. Advances in Intelligent Systems and Computing, vol. 800, pp. 357–367. Springer, Cham (2019)

    Google Scholar 

  • Vazquez, F.: Opinion dynamics on coevolving networks. In: Mukherjee, A., Choudhury, M., Peruani, F., Ganguly, N., Mitra, B. (eds.) Dynamics On and Of Complex Networks, Volume 2, Modeling and Simulation in Science, Engineering and Technology, pp. 89–107. Springer, New York, (2013)

    Google Scholar 

  • Vazquez, F., Gonzalez-Avella, J.C., Eguíluz, V.M., San Miguel, M.: Time-scale competition leading to fragmentation and recombination transitions in the coevolution of network and states. Phys. Rev. E 76, 046120 (2007)

    Article  Google Scholar 

  • Wijs, A., Katoen, J.P., Bošnacki, D.: Efficient GPU algorithms for parallel decomposition of graphs into strongly connected and maximal end components. Formal Methods Syst. Des. 48, 274–300 (2016)

    Article  Google Scholar 

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  1. Social AI Group, Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, Noord-Holland, The Netherlands

    Jan Treur

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  1. Jan Treur
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Treur, J. (2020). Relating a Reified Adaptive Network’s Structure to Its Emerging Behaviour for Bonding by Homophily. In: Network-Oriented Modeling for Adaptive Networks: Designing Higher-Order Adaptive Biological, Mental and Social Network Models. Studies in Systems, Decision and Control, vol 251. Springer, Cham. https://doi.org/10.1007/978-3-030-31445-3_13

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  • DOI: https://doi.org/10.1007/978-3-030-31445-3_13

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