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The Costs of Overambitious Seeding of Social Products

  • Shankar Iyer
  • Lada A. Adamic
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 813)

Abstract

Product-adoption scenarios are often theoretically modeled as “influence-maximization” (IM) problems, where people influence one another to adopt and the goal is to find a limited set of people to “seed” so as to maximize long-term adoption. In many IM models, if there is no budgetary limit on seeding, the optimal approach involves seeding everybody immediately. Here, we argue that this approach can lead to suboptimal outcomes for “social products” that allow people to communicate with one another. We simulate a simplified model of social-product usage where people begin using the product at low rates and then ramp their usage up or down depending upon whether they are satisfied with their experiences. We show that overambitious seeding can result in people adopting in suboptimal contexts, where their friends are not active often enough to produce satisfying experiences. We demonstrate that gradual seeding strategies can do substantially better in these regimes.

Keywords

Product adoption Influence maximization Social networks 

Notes

Acknowledgements

We thank Udi Weinsberg and Israel Nir for helpful discussions, Shuyang Lin for development of the original simulation infrastructure, and Justin Cheng for reviewing code.

References

  1. 1.
    Abebe, R., Adamic, L., Kleinberg, J.: Mitigating overexposure in viral marketing. In: Proceedings of the 32nd Conference on Artificial Intelligence. AAAI (2018)Google Scholar
  2. 2.
    Alkemade, F., Castaldi, C.: Strategies for the diffusion of innovations on social networks. Comput. Econ. 25(1–2), 3–23 (2005)CrossRefGoogle Scholar
  3. 3.
    Centola, D.: How Behavior Spreads: The Science of Complex Contagions, vol. 3. Princeton University Press, Princeton (2018)CrossRefGoogle Scholar
  4. 4.
    Fortunato, S., Hric, D.: Community detection in networks: a user guide. Phys. Rep. 659, 1–44 (2016)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Hagberg, A., Swart, P., Chult, S.D.: Exploring network structure, dynamics, and function using networkX. Technical report, Los Alamos National Laboratory (LANL), Los Alamos, NM (United States) (2008)Google Scholar
  6. 6.
    Jankowski, J., Bródka, P., Kazienko, P., Szymanski, B.K., Michalski, R., Kajdanowicz, T.: Balancing speed and coverage by sequential seeding in complex networks. Scientific reports 7(1), 891 (2017)CrossRefGoogle Scholar
  7. 7.
    Juul, J.S., Porter, M.A.: Hipsters on networks: How a small group of individuals can lead to an anti-establishment majority. arXiv preprint arXiv:1707.07187 (2017)
  8. 8.
    Kabiljo, I., Karrer, B., Pundir, M., Pupyrev, S., Shalita, A.: Social hash partitioner: a scalable distributed hypergraph partitioner. Proceedings of the VLDB Endowment 10(11), 1418–1429 (2017)CrossRefGoogle Scholar
  9. 9.
    Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 137–146. ACM (2003)Google Scholar
  10. 10.
    Kiesling, E., Günther, M., Stummer, C., Wakolbinger, L.M.: Agent-based simulation of innovation diffusion: a review. Central European Journal of Operations Research 20(2), 183–230 (2012)CrossRefGoogle Scholar
  11. 11.
    Kim, J.H., Vu, V.H.: Generating random regular graphs. In: Proceedings of the 35th Annual ACM symposium on Theory of Computing, pp. 213–222. ACM (2003)Google Scholar
  12. 12.
    Li, Y., Fan, J., Wang, Y., Tan, K.L.: Influence maximization on social graphs: A survey. IEEE Transactions on Knowledge and Data Engineering (2018)Google Scholar
  13. 13.
    Moore, C., et al.: The computer science and physics of community detection: landscapes, phase transitions, and hardness. Bull. EATCS 1(121) (2017)Google Scholar
  14. 14.
    Sela, A., Shmueli, E., Goldenberg, D., Ben-Gal, I.: Why spending more might get you less, dynamic selection of influencers in social networks. In: IEEE International Conference on the Science of Electrical Engineering (ICSEE), pp. 1–4. IEEE (2016)Google Scholar
  15. 15.
    Shalita, A., et al.: Social hash: an assignment framework for optimizing distributed systems operations on social networks. In: NSDI, pp. 455–468 (2016)Google Scholar
  16. 16.
    Steger, A., Wormald, N.C.: Generating random regular graphs quickly. Comb. Probab. Comput. 8(4), 377–396 (1999)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Ugander, J., Backstrom, L., Marlow, C., Kleinberg, J.: Structural diversity in social contagion. Proc. Natl. Acad. Sci. 109(16), 5962–5966 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Facebook, Inc.Menlo ParkUSA

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