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Part of the book series: Surveys and Tutorials in the Applied Mathematical Sciences ((STAMS,volume 7))

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Abstract

In this chapter we consider a number of ordinary differential equation models for diffusion of innovation and epidemiological models. We discuss the classical theory on diffusion of innovation, emphasizing online social networks and analyzing several ordinary differential equation models for innovation diffusion. We also present a number of basic compartment epidemiological models and their applications in online social networks, and finally we discuss SIR models and their extensions when the total population is not constant.

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References

  1. Brauer, F., Castillo-Chvez, C.: Mathematical Models in Population Biology and Epidemiology, 2nd edn. Springer, New York (2012)

    Book  Google Scholar 

  2. Brauer, F., Van den Driessche, P., Wu, J.: Mathematical Epidemiology. Springer, Heidelberg (2008)

    Book  Google Scholar 

  3. Dredze, M., Paul, M., Bergsma, S., Tran, H.: Carmen: a Twitter geolocation system with applications to public health. In: AAAI Workshop on Expanding the Boundaries of Health Informatics Using AI (HIAI), pp. 20–24 (2013)

    Google Scholar 

  4. Heesterbeek, J., Metz, J.: The saturating contact rate in marriage and epidemic models. J. Math. Biol. 31, 529–539 (1993)

    Article  MathSciNet  Google Scholar 

  5. Ince, E.L.: Ordinary Differential Equation. Dover, New York (1927)

    MATH  Google Scholar 

  6. Katz, E.: The two-step flow of communication: an up-to-date report on a hypothesis. Public Opin. Q. 21, 61–78 (1957)

    Article  Google Scholar 

  7. Katz, E., Lazarsfeld, P.F.: Personal Influence: The Part Played by People in the Flow of Mass Communications. Transaction Publishers, Piscataway (2006)

    Google Scholar 

  8. Kermack, W.O., McKendrick, A.G.: A contribution to the mathematical theory of epidemics. Proc. R. Soc. Lond. B 115, 700–721 (1927)

    MATH  Google Scholar 

  9. Leskovec, J., Mcglohon, M., Faloutsos, C., Glance, N., Hurst, M.: Cascading behavior in large blog graphs. In: SIAM International Conference on Data Mining (SDM), pp. 551–556 (2007)

    Google Scholar 

  10. Mahajan, V., Peterson, R. (Eds.): Models for Innovation Diffusion, vol. 48. Sage, Newbury Park (1985)

    Google Scholar 

  11. Mena-Lorca, J., Hethcote, H.W.: Dynamic models of infectious diseases as regulators of population sizes. J. Math. Biol. 30, 693–716 (1992)

    MathSciNet  MATH  Google Scholar 

  12. Murray, J.D.: Mathematical Biology I: An Introduction. Springer, New York (1989)

    Book  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  14. Radford, S.: Linking innovation to design: consumer responses to visual product newness. J. Prod. Innov. Manag. 28, 208–220 (2011)

    Article  Google Scholar 

  15. Rogers, E.M.: Diffusion of Innovations, 4th edn. Free Press, New York (1995)

    Google Scholar 

  16. Wang, X-S., Wang, H., Wu, J.: Traveling waves of diffusive predator-prey systems: disease outbreak propagation. Discrete Contin. Dyn. Syst. A 32, 3303–3324 (2012)

    Article  MathSciNet  Google Scholar 

  17. Wayant, N., Crooks, A., Stefanidis, A., Croitoru, A., Radzikowski, J., Stahl, J., Shine, J.: Spatiotemporal clustering of Twitter feeds for activity summarization. In: International Conference on Geographic Information Science, pp. 1–6 (2012)

    Google Scholar 

  18. Zhang, L., Zhong, X., Wan, L.: Modeling structure evolution of online social networks. In: 2012 8th International Conference on Information Science and Digital Content Technology (ICIDT), pp. 15–19. IEEE, Piscataway (2012)

    Google Scholar 

  19. Zhang, X., Sun, G.-Q., Zhu, Y.-X., Ma, J., Jin, Z.: Epidemic dynamics on semi-directed complex networks. Math. Biosci. 246, 242–251 (2013)

    Article  MathSciNet  Google Scholar 

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Wang, H., Wang, F., Xu, K. (2020). Ordinary Differential Equation Models on Social Networks. In: Modeling Information Diffusion in Online Social Networks with Partial Differential Equations. Surveys and Tutorials in the Applied Mathematical Sciences, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-030-38852-2_2

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