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Diffusion in networks and the virtue of burstiness

Author

Listed:
  • Mohammad Akbarpour

    (Graduate School of Business, Stanford University, Stanford, CA 94305)

  • Matthew O. Jackson

    (Department of Economics, Stanford University, Stanford, CA 94305; Santa Fe Institute, Santa Fe, NM 87501; Institutions, Organizations, and Growth (IOG) Program, Canadian Institute for Advanced Research, Toronto, ON, Canada M5G 1Z8)

Abstract

Whether an idea, information, or infection diffuses throughout a society depends not only on the structure of the network of interactions, but also on the timing of those interactions. People are not always available to interact with others, and people differ in the timing of when they are active. Some people are active for long periods and then inactive for long periods, while others switch more frequently from being active to inactive and back. We show that maximizing diffusion in classic contagion processes requires heterogeneous activity patterns across agents. In particular, maximizing diffusion comes from mixing two extreme types of people: those who are stationary for long periods of time, changing from active to inactive or back only infrequently, and others who alternate frequently between being active and inactive.

Suggested Citation

  • Mohammad Akbarpour & Matthew O. Jackson, 2018. "Diffusion in networks and the virtue of burstiness," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 115(30), pages 6996-7004, July.
    • Handle: RePEc:nas:journl:v:115:y:2018:p:e6996-e7004
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    Citations

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    Cited by:

    1. Donsimoni Jean Roch & Wälde Klaus & Glawion René & Plachter Bodo, 2020. "Projecting the spread of COVID-19 for Germany," German Economic Review, De Gruyter, vol. 21(2), pages 181-216, June.
    2. Benjamin Batinge & Josephine Kaviti Musango & Fabrizio Ceschin & Amollo Ambole & Suzanne Smit & Aine Petrulaityte, 2022. "Modelling gendered innovation for the security of energy services in poor urban environments," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(2), pages 287-304, March.
    3. Jeffrey Ely & Andrea Galeotti & Jakub Steiner, 2021. "Rotation as Contagion Mitigation," Management Science, INFORMS, vol. 67(5), pages 3117-3126, May.
    4. Sonin, Konstantin & Egorov, Georgy, 2019. "Persuasion on Networks," CEPR Discussion Papers 13723, C.E.P.R. Discussion Papers.
    5. Has van Vlokhoven, 2023. "Diffusion of Ideas in Networks with Endogenous Search," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 49, pages 269-311, July.
    6. Tiago A. Schieber & Laura C. Carpi & Panos M. Pardalos & Cristina Masoller & Albert Díaz-Guilera & Martín G. Ravetti, 2023. "Diffusion capacity of single and interconnected networks," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    7. Wu, Lianren & Qi, Jiayin & Shi, Nan & Li, Jinjie & Yan, Qiang, 2022. "Revealing the relationship of topics popularity and bursty human activity patterns in social temporal networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    8. Gong, Qingbin & Diao, Xundi, 2023. "The impacts of investor network and herd behavior on market stability: Social learning, network structure, and heterogeneity," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1388-1398.

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