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Just a Few Seeds More: Value of Network Information for Diffusion

Author

Listed:
  • Akbarpour, Mohammad

    (Stanford U)

  • Malladi, Suraj

    (Stanford U)

  • Saberi, Amin

    (Stanford U)

Abstract

Identifying the optimal set of individuals to first receive information ('seeds') in a social network is a widely-studied question in many settings, such as the diffusion of information, microfinance programs, and new technologies. Numerous studies have proposed various network-centrality based heuristics to choose seeds in a way that is likely to boost diffusion. Here we show that, for some frequently studied diffusion processes, randomly seeding s plus x individuals can prompt a larger cascade than optimally targeting the best s individuals, for a small x. We prove our results for large classes of random networks, but also show that they hold in simulations over several real-world networks. This suggests that the returns to collecting and analyzing network information to identify the optimal seeds may not be economically significant. Given these findings, practitioners interested in communicating a message to a large number of people may wish to compare the cost of network-based targeting to that of slightly expanding initial outreach.

Suggested Citation

  • Akbarpour, Mohammad & Malladi, Suraj & Saberi, Amin, 2018. "Just a Few Seeds More: Value of Network Information for Diffusion," Research Papers 3678, Stanford University, Graduate School of Business.
    • Handle: RePEc:ecl:stabus:3678
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    Citations

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    Cited by:

    1. Luca P. Merlino & Nicole Tabasso, 2022. "Optimal Inspection of Rumors in Networks," Working Papers 2022: 19, Department of Economics, University of Venice "Ca' Foscari".
    2. Kolumbus, Yoav & Solomon, Sorin, 2021. "On the influence maximization problem and the percolation phase transition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    3. Luca P. Merlino & Paolo Pin & Nicole Tabasso, 2023. "Debunking Rumors in Networks," American Economic Journal: Microeconomics, American Economic Association, vol. 15(1), pages 467-496, February.
    4. Beatrice Nöldeke & Etti Winter & Ulrike Grote, 2020. "Seed Selection Strategies for Information Diffusion in Social Networks: An Agent-Based Model Applied to Rural Zambia," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 23(4), pages 1-9.
    5. Elias Carroni & Paolo Pin & Simone Righi, 2020. "Bring a Friend! Privately or Publicly?," Management Science, INFORMS, vol. 66(5), pages 2269-2290, May.
    6. Sonin, Konstantin & Egorov, Georgy, 2019. "Persuasion on Networks," CEPR Discussion Papers 13723, C.E.P.R. Discussion Papers.
    7. Deserranno, Erika & Bandiera, Oriana & Rasul, Imran, 2020. "Development Policy through the Lens of Social Structure," CEPR Discussion Papers 14876, C.E.P.R. Discussion Papers.
    8. Tim Roughgarden & Inbal Talgam-Cohen, 2018. "Approximately Optimal Mechanism Design," Papers 1812.11896, arXiv.org, revised Aug 2020.
    9. Mobarak, Ahmed & Levinsohn, James & Guiteras, Raymond, 2019. "Demand Estimation with Strategic Complementarities: Sanitation in Bangladesh," CEPR Discussion Papers 13498, C.E.P.R. Discussion Papers.

    More about this item

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • O12 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Microeconomic Analyses of Economic Development
    • Z13 - Other Special Topics - - Cultural Economics - - - Economic Sociology; Economic Anthropology; Language; Social and Economic Stratification

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