Discount Targeting in Online Social Networks Using Backpressure-Based Learning

Part of the Springer Optimization and Its Applications book series (SOIA, volume 58)


Online social networks are increasingly being seen as a means of obtaining awareness of user preferences. Such awareness could be used to target goods and services at them. We consider a general user model, wherein users could buy different numbers of goods at a marked and at a discounted price. Our first objective is to learn which users would be interested in a particular good. Second, we would like to know how much to discount these users such that the entire demand is realized, but not so much that profits are decreased. We develop algorithms for multihop forwarding of discount coupons over an online social network, in which users forward such coupons to each other in return for a reward. Coupling this idea with the implicit learning associated with backpressure routing (originally developed for multihop wireless networks), we show how to realize optimal revenue. Using simulations, we illustrate its superior performance as compared to random coupon forwarding on different social network topologies. We then propose a simpler heuristic algorithm and using simulations, and show that its performance approaches that of backpressure routing.


Queue Length Online Social Network Discount Price Small Time Scale Optimization Decomposition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Research was funded in part by NSF grant CNS-0904520 and Qatar Telecom, Doha, Qatar.


  1. 1.
    Abe, N., Biermann, A., Long, P.: Reinforcement learning with immediate rewards and linear hypotheses. Algorithmica 37(4), 263–293 (2003)MathSciNetMATHCrossRefGoogle Scholar
  2. 2.
    Armengol, A.C., Jackson, M.O.: The effects of social networks on employment and inequality. American Economic Review 94(3), 426–454 (2004)CrossRefGoogle Scholar
  3. 3.
    Auer, P., Cesa-Bianchi, N., Freund, Y., Schapire, R.: The nonstochastic multiarmed bandit problem. SIAM Journal on Computing 32(1), 48–77 (2003)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Bala, V., Goyal, S.: Learning from neighbors. Review of Economic Studies 65, 595–621 (1998)MATHCrossRefGoogle Scholar
  5. 5.
    Banks, D., Carley, K.: Metric inference for social networks. Journal of Classification (Springer) 11(1), 121–149 (1994)Google Scholar
  6. 6.
    Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286, 509–512 (1999)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Boyd, D.M., Ellison, N.B.: Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication 13(1) (2007)Google Scholar
  8. 8.
    Bramoulle, Y., Kranton, R.: A model of public goods: Experimentation and social learning, vol. 135 (2007)Google Scholar
  9. 9.
    Chen, L., Low, S.H., Chiang, M., Doyle, J.C.: Cross-layer congestion control, routing and scheduling design in ad hoc wireless networks. In: IEEE Infocom. Barcelona, Spain (2006)Google Scholar
  10. 10.
    Chiang, M., Low, S.H., Calderbank, A.R., Doyle, J.C.: Layering as optimization decomposition: A mathematical theory of network architectures. In: Proceedings of the IEEE, pp. 255–312 (2007)Google Scholar
  11. 11.
    Choi, S., Gale, D., Kariv, S.: Behavioral aspects of learning in social networks: An experimental study. Advances in Applied Microeconomics 13 (2005)Google Scholar
  12. 12.
    Clauset, A., Moore, C., Newman, M.E.J.: Hierarchical structure and the prediction of missing links in networks. Nature 453, 98–101 (2008)CrossRefGoogle Scholar
  13. 13.
    Eryilmaz, A., Srikant, R.: Joint Congestion Control, Routing and MAC for Stability and Fairness in Wireless Networks. IEEE Journal on Selected Areas in Communications 24(8), 1514–1524 (2006)Google Scholar
  14. 14.
  15. 15.
    Fiore, A., Donath, J.: Homophily in online dating: When do you like someone like yourself? In: Proceedings of the ACM Conference on Human Factors in Computing Systems, pp. 1371–1374. New York, NY, USA (2005)Google Scholar
  16. 16.
  17. 17.
    Gale, D., Kariv, S.: Bayesian learning in social networks. Games and Economic Behavior 45(2), 329–346 (2003)MathSciNetMATHCrossRefGoogle Scholar
  18. 18.
    Georgiadis, L., Neely, M.J., Tassiulas, L.: Resource Allocation and Cross-Layer Control in Wireless Networks. Foundations and Trends in Networking. Now Publishes, Delft, The NetherlandsGoogle Scholar
  19. 19.
    Jackson, M.O., Wolinsky: A strategic model of social and economic networks. J. Economic Theory 71(1), 44–74 (1996)MATHCrossRefGoogle Scholar
  20. 20.
    Joffe, B.: New business models in online communities. In: Proceedings of Media’08. Sydney, Australia (2008)Google Scholar
  21. 21.
    Kelly, F.P.: Multi-armed bandits with discount factor near one: The Bernoulli case. Adv. Appl. Prob. 9, 897–1001 (1982)Google Scholar
  22. 22.
    Kelly, F.P.: Charging and rate control for elastic traffic. European Transactions on Telecommunications 8, 33–37 (1997)CrossRefGoogle Scholar
  23. 23.
    Kelly, F.P.: Models for a self-managed Internet. Philosophical Transactions of the Royal Society A358, 2335–2348 (2000)Google Scholar
  24. 24.
    Kelly, F.P.: Mathematical modelling of the Internet. In: Mathematics Unlimited - 2001 and Beyond (Editors B. Engquist and W. Schmid), pp. 685–702. Springer-Verlag, Berlin (2001)Google Scholar
  25. 25.
    Kelly, F.P., Maulloo, A., Tan, D.: Rate control in communication networks: Shadow prices, proportional fairness and stability. J. Operational Research Society. 49, 237–252 (1998)MATHGoogle Scholar
  26. 26.
    Lin, X., Shroff, N., Srikant, R.: A tutorial on cross-layer optimization in wireless networks. IEEE J. Sel. Areas Commun. (2006)Google Scholar
  27. 27.
    Liu, H.: Social network profiles as taste performances. Journal of Computer-Mediated Communication 13(1) (2007)Google Scholar
  28. 28.
    Liu, H., Maes, P., Davenport, G.: Unraveling the taste fabric of social networks. International Journal on Semantic Web and Information Systems 2(1) (2006)Google Scholar
  29. 29.
    Low, S.H., Lapsley, D.E.: Optimization flow control, I: Basic algorithm and convergence. IEEE/ACM Trans. Network. 7(6), 861–875 (1999)Google Scholar
  30. 30.
    Lu, M.: Net group wants action on spam. Taipai Times. (2008).
  31. 31.
  32. 32.
  33. 33.
    Neely, M., Modiano, E., Li, C.: Fairness and optimal stochastic control for heterogeneous networks. In: Proc. IEEE Infocom., vol. 3, pp. 1723–1734. Miami, FL (2005)Google Scholar
  34. 34.
  35. 35.
  36. 36.
    Shakkottai, S., Srikant, R.: Network Optimization and Control. Foundations and Trends in Networking. Now Publishes, Delft, The Netherlands (2008)Google Scholar
  37. 37.
    Spertus, E., Sahami, M., Büyükkökten, O.: Evaluating similarity measures: A large-scale study in the Orkut social network. In: Proceedings of 11th International Conference on Knowledge Discovery in Data Mining, pp. 678–684. New York, NY, USA (2005)Google Scholar
  38. 38.
    Srikant, R.: The Mathematics of Internet Congestion Control. Birkhauser, Boston, MA (2004)MATHCrossRefGoogle Scholar
  39. 39.
    Stolyar, A.: Maximizing queueing network utility subject to stability: Greedy primal-dual algorithm. Queueing Systems 50(4), 401–457 (2005)MathSciNetMATHCrossRefGoogle Scholar
  40. 40.
    Tassiulas, L., Ephremides, A.: Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks. IEEE Transactions on Automatic Control pp. 1936–1948 (1992)Google Scholar

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© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringTexas A&M UniversityCollege StationUSA
  2. 2.Department of Electrical and Computer EngineeringIowa State UniversityAmesUSA

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