Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
126 tokens/sec
GPT-4o
47 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Keeping Up With the Winner! Targeted Advertisement to Communities in Social Networks (2403.19903v1)

Published 29 Mar 2024 in eess.SY, cs.SI, and cs.SY

Abstract: When a new product enters a market already dominated by an existing product, will it survive along with this dominant product? Most of the existing works have shown the coexistence of two competing products spreading/being adopted on overlaid graphs with same set of users. However, when it comes to the survival of a weaker product on the same graph, it has been established that the stronger one dominates the market and wipes out the other. This paper makes a step towards narrowing this gap so that a new/weaker product can also survive along with its competitor with a positive market share. Specifically, we identify a locally optimal set of users to induce a community that is targeted with advertisement by the product launching company under a given budget constraint. To this end, we model the system as competing Susceptible-Infected-Susceptible (SIS) epidemics and employ perturbation techniques to quantify and attain a positive market share in a cost-efficient manner. Our extensive simulation results with real-world graph dataset show that with our choice of target users, a new product can establish itself with positive market share, which otherwise would be dominated and eventually wiped out of the competitive market under the same budget constraint.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (56)
  1. Polar opinion dynamics in social networks. IEEE Transactions on Automatic Control, 62(11): 5650–5665.
  2. Biased opinion dynamics: when the devil is in the details. Information Sciences, 593: 49–63.
  3. Marketing: an introduction. Pearson Educación.
  4. Interacting viruses in networks: can both survive? In ACM SIGKDD.
  5. A Brauer’s theorem and related results. Central European Journal of Mathematics, 10: 312–321.
  6. Node immunization on large graphs: Theory and algorithms. IEEE Transactions on Knowledge and Data Engineering, 28(1): 113–126.
  7. Scalable influence maximization in social networks under the linear threshold model. In IEEE international conference on data mining, 88–97.
  8. Contributor, G. 2024. What Are Influencer Pods? – Everything You Need to Know. https://grin.co/blog/influencer-pods/. Accessed: 2024-03-22.
  9. New products: what separates winners from losers? Journal of Product Innovation Management, 4(3): 169–184.
  10. Niche marketing revisited: concept, applications and some European cases. European journal of marketing, 28(4): 39–55.
  11. Biased assimilation, homophily, and the dynamics of polarization. Proceedings of the National Academy of Sciences, 110(15): 5791–5796.
  12. Competing Epidemics on Graphs–Global Convergence and Coexistence. In IEEE INFOCOM.
  13. Freeman, L. C. 1978. Centrality in social networks conceptual clarification. Social networks, 1(3): 215–239.
  14. French Jr, J. R. 1956. A formal theory of social power. Psychological review, 63(3): 181.
  15. Friedkin, N. E. 1991. Theoretical foundations for centrality measures. American journal of Sociology, 96(6): 1478–1504.
  16. Evaluating the effectiveness of brand-positioning strategies from a consumer perspective. European Journal of Marketing, 44(11/12): 1763–1786.
  17. Innovation and competition in standard-based industries: A historical analysis of the US home video game market. IEEE transactions on engineering management, 49(1): 67–82.
  18. Computers and intractability, volume 174. Freeman San Francisco.
  19. The saturation threshold of public opinion: are aggressive media campaigns always eective? In Proceedings of ESSA 2008 Conference.
  20. Granovetter, M. 1978. Threshold models of collective behavior. American journal of sociology, 83(6): 1420–1443.
  21. First-order perturbation theory for eigenvalues and eigenvectors. SIAM review, 62(2): 463–482.
  22. Airbus versus Boeing revisited: international competition in the aircraft market. Journal of international economics, 64(2): 223–245.
  23. Brand positioning strategies for industrial firms providing customer solutions. Journal of Business & Industrial Marketing, 29(3): 253–264.
  24. Kalish, S. 1985. A new product adoption model with price, advertising, and uncertainty. Management science, 31(12): 1569–1585.
  25. Maximizing the spread of influence through a social network. In Proceedings of the ninth ACM SIGKDD, 137–146.
  26. A deterministic model for gonorrhea in a nonhomogeneous population. Mathematical Biosciences, 28(3-4): 221–236.
  27. SNAP Datasets: Stanford Large Network Dataset Collection. http://snap.stanford.edu/data. Accessed: 2022-02-20.
  28. Susceptible-infected-susceptible model: A comparison of N-intertwined and heterogeneous mean-field approximations. Physical Review E, 86(2): 026116.
  29. Analysis and control of a continuous-time bi-virus model. IEEE Transactions on Automatic Control, 64(12): 4891–4906.
  30. The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations. Behavioral Ecology and Sociobiology, 54(4): 396–405.
  31. Learning to discover social circles in ego networks. In NIPS, volume 2012, 548–56.
  32. Meyer, C. D. 2000. Matrix Analysis and Applied Linear Algebra, volume 71. SIAM.
  33. Mobilia, M. 2015. Nonlinear q-voter model with inflexible zealots. Physical Review E, 92(1): 012803.
  34. Determinants of new product performance: A review and meta-analysis. Journal of product innovation management, 11(5): 397–417.
  35. Voter and majority dynamics with biased and stubborn agents. Journal of Statistical Physics, 181: 1239–1265.
  36. Newman, M. 2018. Networks. Oxford university press.
  37. Dynamics of opinion formation under majority rules on complex social networks. Scientific reports, 10(1): 456.
  38. Nic Healey. 2013. Battle of the ad campaign: PS4 vs Xbox One. https://www.cnet.com/tech/gaming/battle-of-the-ad-campaign-ps4-vs-xbox-one/. Accessed: 2023-01-12.
  39. Epidemic spreading in networks—variance of the number of infected nodes. Delft University of Technology, report20090707. http://www. nas. ewi. tudelft. nl/people/Piet/TUDelftReports.
  40. A generalized linear threshold model for multiple cascades. In IEEE International Conference on Data Mining, 965–970.
  41. Winner takes all: competing viruses or ideas on fair-play networks. In ACM World Wide Web.
  42. A tutorial on modeling and analysis of dynamic social networks. Part I. Annual Reviews in Control, 43: 65–79.
  43. A tutorial on modeling and analysis of dynamic social networks. Part II. Annual Reviews in Control, 45: 166–190.
  44. Reddit. 2009. Reddit. https://www.reddit.com. Accessed: 2022-10-10.
  45. The Network Data Repository with Interactive Graph Analytics and Visualization. In AAAI.
  46. Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management, 1325–1334.
  47. Competitive epidemic spreading over arbitrary multilayer networks. Physical Review E, 89(6): 062817.
  48. Bi-virus SIS epidemics over networks: Qualitative analysis. IEEE Transactions on Network Science and Engineering, 2(1): 17–29.
  49. Smith, H. L. 1995. Monotone dynamical systems: an introduction to the theory of competitive and cooperative systems. 41. American Mathematical Soc.
  50. Virus spread in networks. IEEE/ACM Transactions On Networking, 17(1): 1–14.
  51. Epidemic spreading in real networks: An eigenvalue viewpoint. In IEEE Reliable Distributed Systems.
  52. Social network analysis: Methods and applications. (No Title).
  53. Cooperative spreading processes in multiplex networks. Chaos: An Interdisciplinary Journal of Nonlinear Science, 26(6).
  54. Convergence to global consensus in opinion dynamics under a nonlinear voter model. Physics Letters A, 376(4): 282–285.
  55. A bi-virus competing spreading model with generic infection rates. IEEE Transactions on Network Science and Engineering, 5(1): 2–13.
  56. Mining social networks for targeted advertising. In Proceedings of the 39th Annual Hawaii International Conference on System Sciences, volume 6, 137a–137a.

Summary

We haven't generated a summary for this paper yet.