Skip to main content
SpringerLink
Log in

Pricing Strategies for Maximizing Viral Advertising in Social Networks

  • Conference paper
  • First Online:
Database Systems for Advanced Applications (DASFAA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9050))

Included in the following conference series:

Abstract

Viral advertising in social networks is playing an important role for the promotions of new products, ideas and innovations. It usually starts from a set of initial adopters and spreads via social links to become viral. Given a limited budget, one central problem in studying viral advertising is influence maximization, in which one needs to target a set of initial adopters such that the number of users accepting the advertising afterwards is maximized. To solve this problem, previous works assume that each user has a fixed cost and will spread the advertising as long as the provider offers a benefit that is equal to the cost. However, the assumption is oversimplified and far from real scenarios. In practice, it is crucial for the provider to understand how to incentivize the initial adopters.

In this paper, we propose the use of concave probability functions to model the user valuation for sharing the advertising. Under the new pricing model, we show that it is NP-hard to find the optimal pricing strategy. Due to the hardness, we then propose a discrete greedy pricing strategy which has a constant approximation performance guarantee. We also discuss how to discretize the budget to provide a good trade-off between the performance and the efficiency. Extensive experiments on different data sets are implemented to validate the effectiveness of our algorithm in practice.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Akhlaghpour, H., Ghodsi, M., Haghpanah, N., Mirrokni, V.S., Mahini, H., Nikzad, A.: Optimal iterative pricing over social networks (extended abstract). In: Saberi, A. (ed.) WINE 2010. LNCS, vol. 6484, pp. 415–423. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  2. Anagnostopoulos, A., Kumar, R., Mahdian, M.: Influence and correlation in social networks. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 7–15. ACM (2008)

    Google Scholar 

  3. Arthur, D., Motwani, R., Sharma, A., Xu, Y.: Pricing strategies for viral marketing on social networks. In: Leonardi, S. (ed.) WINE 2009. LNCS, vol. 5929, pp. 101–112. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  4. Bakshy, E., Karrer, B., Adamic, L.A.: Social influence and the diffusion of user-created content. In: Proceedings of the 10th ACM Conference on Electronic Commerce, pp. 325–334. ACM (2009)

    Google Scholar 

  5. Candogan, O., Bimpikis, K., Ozdaglar, A.: Optimal pricing in networks with externalities. Operations Research 60(4), 883–905 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  6. Cha, M., Haddadi, H., Benevenuto, F., Gummadi, P.K.: Measuring user influence in twitter: The million follower fallacy. In: ICWSM 2010, pp. 10–17 (2010)

    Google Scholar 

  7. Chen, W., Lu, P., Sun, X., Tang, B., Wang, Y., Zhu, Z.A.: Optimal pricing in social networks with incomplete information. In: Chen, N., Elkind, E., Koutsoupias, E. (eds.) WINE 2011. LNCS, vol. 7090, pp. 49–60. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  8. Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 199–208. ACM (2009)

    Google Scholar 

  9. Chierichetti, F., Kleinberg, J., Panconesi, A.: How to schedule a cascade in an arbitrary graph. In: Proceedings of the 13th ACM Conference on Electronic Commerce, pp. 355–368. ACM (2012)

    Google Scholar 

  10. Demaine, E.D., Hajiaghayi, M., Mahini, H., Malec, D.L., Raghavan, S., Sawant, A., Zadimoghadam, M.: How to influence people with partial incentives. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 937–948. International World Wide Web Conferences Steering Committee (2014)

    Google Scholar 

  11. Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 57–66. ACM (2001)

    Google Scholar 

  12. Hartline, J., Mirrokni, V., Sundararajan, M.: Optimal marketing strategies over social networks. In: Proceedings of the 17th International Conference on World Wide Web, pp. 189–198. ACM (2008)

    Google Scholar 

  13. Ioannidis, S., Chaintreau, A., Massoulié, L.: Optimal and scalable distribution of content updates over a mobile social network. In: INFOCOM 2009, pp. 1422–1430. IEEE (2009)

    Google Scholar 

  14. 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 

  15. Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., Glance, N.: Cost-effective outbreak detection in networks. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 420–429. ACM (2007)

    Google Scholar 

  16. Lin, S., Wang, F., Hu, Q., Yu, P.S.: Extracting social events for learning better information diffusion models. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 365–373. ACM (2013)

    Google Scholar 

  17. Marshall, A.: Principles of economics. Digireads. com (2004)

    Google Scholar 

  18. Nemhauser, G.L., Wolsey, L.A., Fisher, M.L.: An analysis of approximations for maximizing submodular set functions. Mathematical Programming 14(1), 265–294 (1978)

    Article  MATH  MathSciNet  Google Scholar 

  19. Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 61–70. ACM (2002)

    Google Scholar 

  20. Sahni, S.: Computationally related problems. SIAM Journal on Computing 3(4), 262–279 (1974)

    Article  MathSciNet  Google Scholar 

  21. Singer, Y.: How to win friends and influence people, truthfully: influence maximization mechanisms for social networks. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, pp. 733–742. ACM (2012)

    Google Scholar 

  22. Singer, Y., Mittal, M.: Pricing mechanisms for crowdsourcing markets. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 1157–1166. International World Wide Web Conferences Steering Committee (2013)

    Google Scholar 

  23. Vondrak, J.: Optimal approximation for the submodular welfare problem in the value oracle model. In: Proceedings of the 40th Annual ACM Symposium on Theory of Computing, pp. 67–74. ACM (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Bolei Zhang, Zhuzhong Qian, Wenzhong Li & Sanglu Lu

Authors
  1. Bolei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhuzhong Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenzhong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sanglu Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhuzhong Qian .

Editor information

Editors and Affiliations

  1. Universität München, München, Germany

    Matthias Renz

  2. University of Southern California, Los Angeles, USA

    Cyrus Shahabi

  3. University of Queensland, Brisbane, Australia

    Xiaofang Zhou

  4. Monash University, Clayton, Australia

    Muhammad Aamir Cheema

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Zhang, B., Qian, Z., Li, W., Lu, S. (2015). Pricing Strategies for Maximizing Viral Advertising in Social Networks. In: Renz, M., Shahabi, C., Zhou, X., Cheema, M. (eds) Database Systems for Advanced Applications. DASFAA 2015. Lecture Notes in Computer Science(), vol 9050. Springer, Cham. https://doi.org/10.1007/978-3-319-18123-3_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-18123-3_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18122-6

  • Online ISBN: 978-3-319-18123-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation