The Costs of Overambitious Seeding of Social Products
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.
KeywordsProduct 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.
- 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
- 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
- 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)
- 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
- 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.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.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.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.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