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.
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Notes
- 1.
Other variants of this rule can certainly be considered and may even lead to better long-term outcomes, but this choice suffices to demonstrate our main results.
- 2.
We set \(\langle k_{oc} \rangle < k_{in}\), so the average person has many more friendships within the same cluster than with people in the other clusters.
- 3.
On average, each person in each cluster has at least one out-of-cluster friend.
- 4.
The size of the three-cluster network can differ slightly from the size of the three clusters individually because, in both cases, we exclude people with zero degree, who would inevitably churn under our model. In a small percentage of cases, a person who has no within-cluster friends may still have friends in another cluster when three clusters are considered together.
- 5.
Figure 3 shows the case of \(k_{ic} = 10\), but this approximate pattern holds for \(k_{ic} = 50\) as well.
- 6.
There is only one network (FGH) where single-shot universal seeding wins, and then only at high \(p_0\)
- 7.
- 8.
Although it is definitely questionable whether churn is ever completely irreversible.
References
Abebe, R., Adamic, L., Kleinberg, J.: Mitigating overexposure in viral marketing. In: Proceedings of the 32nd Conference on Artificial Intelligence. AAAI (2018)
Alkemade, F., Castaldi, C.: Strategies for the diffusion of innovations on social networks. Comput. Econ. 25(1–2), 3–23 (2005)
Centola, D.: How Behavior Spreads: The Science of Complex Contagions, vol. 3. Princeton University Press, Princeton (2018)
Fortunato, S., Hric, D.: Community detection in networks: a user guide. Phys. Rep. 659, 1–44 (2016)
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)
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)
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)
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)
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)
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)
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)
Li, Y., Fan, J., Wang, Y., Tan, K.L.: Influence maximization on social graphs: A survey. IEEE Transactions on Knowledge and Data Engineering (2018)
Moore, C., et al.: The computer science and physics of community detection: landscapes, phase transitions, and hardness. Bull. EATCS 1(121) (2017)
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)
Shalita, A., et al.: Social hash: an assignment framework for optimizing distributed systems operations on social networks. In: NSDI, pp. 455–468 (2016)
Steger, A., Wormald, N.C.: Generating random regular graphs quickly. Comb. Probab. Comput. 8(4), 377–396 (1999)
Ugander, J., Backstrom, L., Marlow, C., Kleinberg, J.: Structural diversity in social contagion. Proc. Natl. Acad. Sci. 109(16), 5962–5966 (2012)
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.
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Iyer, S., Adamic, L.A. (2019). The Costs of Overambitious Seeding of Social Products. In: Aiello, L., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L. (eds) Complex Networks and Their Applications VII. COMPLEX NETWORKS 2018. Studies in Computational Intelligence, vol 813. Springer, Cham. https://doi.org/10.1007/978-3-030-05414-4_22
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