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Overexposure-Aware Influence Maximization

Published:06 October 2020Publication History
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Abstract

Viral marketing campaigns are often negatively affected by overexposure. Overexposure occurs when users become less likely to favor a promoted product after receiving information about the product from too large a fraction of their friends. Yet, existing influence diffusion models do not take overexposure into account, effectively overestimating the number of users who favor the product and diffuse information about it. In this work, we propose the first influence diffusion model that captures overexposure. In our model, Latency Aware Independent Cascade Model with Overexposure (LAICO), the activation probability of a node representing a user is multiplied (discounted) by an overexposure score, which is calculated based on the ratio between the estimated and the maximum possible number of attempts performed to activate the node. We also study the influence maximization problem under LAICO. Since the spread function in LAICO is non-submodular, algorithms for submodular maximization are not appropriate to address the problem. Therefore, we develop an approximation algorithm that exploits monotone submodular upper and lower bound functions of spread, and a heuristic that aims to maximize a proxy function of spread iteratively. Our experiments show the effectiveness and efficiency of our algorithms.

References

  1. A. Awan. 2015. Less is more: You are about to receive less email from LinkedIn. Retrieved from https://blog.linkedin.com/2015/07/27/less-email-from-linkedin?sf11404748=1.Google ScholarGoogle Scholar
  2. R. Abebe, L. A. Adamic, and J. Kleinberg. 2018. Mitigating overexposue in viral marketing. In AAAI.Google ScholarGoogle Scholar
  3. F. Alkemade and C. Castaldi. 2005. Strategies for the diffusion of innovations on social networks. Comp. Econ. 25, 1 (2005).Google ScholarGoogle Scholar
  4. N. Bhatti, A. Bouch, and A. Kuchinsky. 2000. Integrating user-perceived quality into web server design. Comput. Netw. 33, 1--6 (2000), 1--16.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. J. A. Bilmes and W. Bai. 2017. Deep submodular functions. CoRR abs/1701.08939 (2017).Google ScholarGoogle Scholar
  6. K. Bontcheva, G. Gorrell, and B. Wessels. 2013. Social media and information overload. CoRR abs/1306.0813 (2013).Google ScholarGoogle Scholar
  7. C. Borgs, M. Brautbar, J. Chayes, and B. Lucier. 2014. Maximizing social influence in nearly optimal time. In SODA.Google ScholarGoogle Scholar
  8. N. Buchbinder, M. Feldman, J. Naor, and R. Schwartz. 2014. Submodular maximization with cardinality constraints. In SODA.Google ScholarGoogle Scholar
  9. W. Chen, W. Lu, and N. Zhang. 2012. Time-critical influence maximization in social networks with time-delayed diffusion process. In AAAI.Google ScholarGoogle Scholar
  10. W. Chen, C. Wang, and Y. Wang. 2010. Scalable influence maximization for prevalent viral marketing in large-scale social networks. In KDD. 1029--1038.Google ScholarGoogle Scholar
  11. W. Chen, Y. Wang, and S. Yang. 2009. Efficient influence maximization in social networks. In KDD. 199--208.Google ScholarGoogle Scholar
  12. P. Dagum, R. Karp, M. Luby, and S. Ross. 2000. An optimal algorithm for monte carlo estimation. SIAM J. Comput. 29, 5 (2000), 1484--1496.Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. S. Feng, X. Chen, G. Cong, Y. Zeng, Y. M. Chee, and Y. Xiang. 2014. Influence maximization with novelty decay in social networks. In AAAI.Google ScholarGoogle Scholar
  14. J. Goldenberg, B. Libai, and E. Muller. 2001. Talk of the network: A complex systems look at the underlying process of word-of-mouth. Marketing Lett. 12 (2001), 211--223.Google ScholarGoogle ScholarCross RefCross Ref
  15. Mark Granovetter. 1978. Threshold models of collective behavior. Amer. J. Sociology 83, 6 (1978), 1420--1443.Google ScholarGoogle ScholarCross RefCross Ref
  16. R. Gwadera and G. Loukides. 2017. Cost-effective viral marketing in the latency aware independent cascade model. In PAKDD. 251--265.Google ScholarGoogle Scholar
  17. R. Iyer and J. Bilmes. 2012. Algorithms for approximate minimization of the difference between submodular functions, with applications. In UAI.Google ScholarGoogle Scholar
  18. J. Jeong and S. Moon. 2014. Invite your friends and get rewards: Dynamics of incentivized friend invitation in kakaotalk mobile games. In ACM COSN.Google ScholarGoogle Scholar
  19. K. Kalyanam, S. McIntyre, and T. J. Masonis. 2007. Adaptive experimentation in interactive marketing: The case of viral marketing at Plaxo. J. of Int. Marketing 21, 3 (2007), 72--85.Google ScholarGoogle Scholar
  20. D. Kempe, J. Kleinberg, and E. Tardos. 2003. Maximizing the spread of influence through a social network. In KDD.Google ScholarGoogle Scholar
  21. M. Kimura, K. Saito, and H. Motoda. 2009. Blocking links to minimize contamination spread in a social network. ACM Trans. Knowl. Discov. Data 3, 2 (2009).Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. A. Krause and D. Golovin. 2013. Submodular function maximization. In Tractability.Google ScholarGoogle Scholar
  23. J. Leskovec, L. A. Adamic, and B. A. Huberman. 2007. The dynamics of viral marketing. ACM Trans. Web 1, 1 (May 2007), 5--es.Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Y. Li, J. Fan, Y. Wang, and K. Tan. 2018. Influence maximization on social graphs: A survey. TKDE 30, 10 (2018), 1852--1872.Google ScholarGoogle ScholarCross RefCross Ref
  25. B. Liu, G. Cong, D. Xu, and Y. Zeng. 2012. Time constrained influence maximization in social networks. In ICDM.Google ScholarGoogle Scholar
  26. G. Loukides and R. Gwadera. 2018. Preventing the diffusion of information to vulnerable users while preserving PageRank. Int. J. Data Sci. Anal. 5, 1 (2018), 19--39.Google ScholarGoogle ScholarCross RefCross Ref
  27. J. J. Louviere, D. A. Hensher, and J. D. Swait. 2000. Stated Choice Methods Analysis and Applications.Google ScholarGoogle Scholar
  28. W. Lu, W. Chen, and L. V. S. Lakshmanan. 2015. From competition to complementarity: Comparative influence diffusion and maximization. PVLDB 9, 2 (2015).Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. M. Minoux. 1978. Accelerated greedy algorithms for maximizing submodular set functions. In Optimization Techniques.Google ScholarGoogle Scholar
  30. M. Mitrovic, M. Bun, A. Krause, and A. Karbasi. 2017. Differentially private submodular maximization: Data summarization in disguise. In ICML.Google ScholarGoogle Scholar
  31. S. A. Myers, C. Zhu, and J. Leskovec. 2012. Information diffusion and external influence in networks. In KDD. 33--41.Google ScholarGoogle Scholar
  32. G. L. Nemhauser, L. A. Wolsey, and M. L. Fisher. 1978. An analysis of approximations for maximizing submodular set functions. Math. Program. 14, 1 (1978), 265--294.Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. N. Ohsaka, Y. Yamaguchi, N. Kakimura, and K. Kawarabayashi. 2016. Maximizing time-decaying influence in social networks. In ECML/PKDD.Google ScholarGoogle Scholar
  34. Ayse Bengi Ozcelik and Kaan Varnali. 2019. Effectiveness of online behavioral targeting: A psychological perspective. Electron. Commerce Res. Appl. 33 (2019), 100819.Google ScholarGoogle ScholarCross RefCross Ref
  35. C. C. Pugh. 2015. Real Mathematical Analysis.Google ScholarGoogle Scholar
  36. D. M. Romero, B. Meeder, and J. Kleinberg. 2011. Differences in the mechanics of information diffusion across topics. In WWW.Google ScholarGoogle Scholar
  37. D. Simchi-Levi, X. Chen, and J. Bramel. 2005. Convexity and Supermodularity. Springer New York.Google ScholarGoogle Scholar
  38. Z. Svitkina and L. Fleischer. 2011. Submodular approximation: Sampling-based algorithms and lower bounds. SIAM J. Comput. 40, 6 (2011), 1715--1737.Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Y. Tang, Y. Shi, and X. Xiao. 2015. Influence maximization in near-linear time: A martingale approach. In SIGMOD.Google ScholarGoogle Scholar
  40. A. Volkova. 1996. A refinement of the central limit theorem for sums of independent random indicators. Theory Probab. Appl. 40, 4 (1996), 791--794.Google ScholarGoogle ScholarCross RefCross Ref
  41. B. Wang, G. Chen, L. Fu, L. Song, and X. Wang. 2017. DRIMUX: Dynamic rumor influence minimization with user experience in social networks. IEEE Trans. Knowl. Data Eng. 29, 10 (2017), 2168--2181.Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. L. Wu, P. Sun, Y. Fu Yanjie, R. Hong, X. Wang, and M. Wang. 2019. A neural influence diffusion model for social recommendation. In SIGIR. 235--244.Google ScholarGoogle Scholar
  43. Y. Zhou and L. Liu. 2013. Social influence based clustering of heterogeneous information networks. In KDD. 338--346.Google ScholarGoogle Scholar
  44. J. Zhu, J. Zhu, S. Ghosh, W. Wu, and J. Yuan. 2019. Social influence maximization in hypergraph in social networks. IEEE Trans. Network Sci. Eng. 6, 4 (2019).Google ScholarGoogle ScholarCross RefCross Ref
  45. Y. Zhu, P. Yin, D. Li, and B. Lin. 2019. Strengthening the positive effect of viral marketing. In ICDCS. 1941--1950.Google ScholarGoogle Scholar

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    • Published in

      cover image ACM Transactions on Internet Technology
      ACM Transactions on Internet Technology  Volume 20, Issue 4
      November 2020
      391 pages
      ISSN:1533-5399
      EISSN:1557-6051
      DOI:10.1145/3427795
      • Editor:
      • Ling Liu
      Issue’s Table of Contents

      Copyright © 2020 ACM

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 6 October 2020
      • Accepted: 1 June 2020
      • Revised: 1 April 2020
      • Received: 1 February 2020
      Published in toit Volume 20, Issue 4

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