The Costs of Overambitious Seeding of Social Products

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


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.


Product adoption Influence maximization Social networks 



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.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Facebook, Inc.Menlo ParkUSA

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