Skip to main content
SpringerLink
Log in
Book cover

International Conference on Web-Age Information Management

WAIM 2015: Web-Age Information Management pp 42–56Cite as

Individual Influence Maximization via Link Recommendation

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9098))

Abstract

Recent years have witnessed the increasing interest in exploiting social influence in social networks for many applications, such as viral marketing. Most of the existing research focused on identifying a subset of influential individuals with the maximum influence spread. However, in the real-world scenarios, many individuals also care about the influence of herself and want to improve it. In this paper, we consider such a problem that maximizing a target individual’s influence by recommending new links. Specifically, if a given individual/node makes new links with our recommended nodes then she will get the maximum influence gain. Along this line, we formulate this link recommendation problem as an optimization problem and propose the corresponding objective function. As it is intractable to obtain the optimal solution, we propose greedy algorithms with a performance guarantee by exploiting the submodular property. Furthermore, we study the optimization problem under a specific influence propagation model (i.e., Linear model) and propose a much faster algorithm (uBound), which can handle large scale networks without sacrificing accuracy. Finally, the experimental results validate the effectiveness and efficiency of our proposed algorithms.

This is a preview of subscription content, 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: KDD (2002)

    Google Scholar 

  2. Chen, W., Lakshmanan, L.V., Castillo, C.: Information and influence propagation in social networks. Synthesis Lectures on Data Management 5(4), 1–177 (2013)

    Article  Google Scholar 

  3. Goldenberg, J., Libai, B., Muller, E.: Talk of the network: A complex systems look at the underlying process of word-of-mouth. Marketing letters 12(3), 211–223 (2001)

    Article  Google Scholar 

  4. Granovetter, M.: Threshold models of collective behavior. American journal of sociology, 1420–1443 (1978)

    Google Scholar 

  5. Aggarwal, C., Khan, A., Yan, X.: On flow authority discovery in social networks. In: SDM, pp. 522–533 (2011)

    Google Scholar 

  6. Xiang, B., Liu, Q., Chen, E., Xiong, H., Zheng, Y., Yang, Y.: Pagerank with priors: an influence propagation perspective. In: IJCAI (2013)

    Google Scholar 

  7. Goyal, A., Bonchi, F., Lakshmanan, L.V.: Learning influence probabilities in social networks. In: WSDM (2010)

    Google Scholar 

  8. Saito, K., Ohara, K., Yamagishi, Y., Kimura, M., Motoda, H.: Learning diffusion probability based on node attributes in social networks. In: Kryszkiewicz, M., Rybinski, H., Skowron, A., Raś, Z.W. (eds.) ISMIS 2011. LNCS, vol. 6804, pp. 153–162. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  9. He, X., Kempe, D.: Stability of influence maximization. In: SIGKDD, pp. 1256–1265 (2014)

    Google Scholar 

  10. Eftekhar, M., Ganjali, Y., Koudas, N.: Information cascade at group scale. In: KDD (2013)

    Google Scholar 

  11. Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., Glance, N.: Cost-effective outbreak detection in networks. In: KDD (2007)

    Google Scholar 

  12. Chaoji, V., Ranu, S., Rastogi, R., Bhatt, R.: Recommendations to boost content spread in social networks. In: WWW (2012)

    Google Scholar 

  13. Domingos, P., Richardson, M.: Mining the network value of customers. In: KDD (2001)

    Google Scholar 

  14. Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: KDD (2003)

    Google Scholar 

  15. Chen, W., Wang, C., Wang, Y.: Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: KDD (2010)

    Google Scholar 

  16. Kim, J., Kim, S.K., Yu, H.: Scalable and parallelizable processing of influence maximization for large-scale social networks? In: ICDE, pp. 266–277. IEEE (2013)

    Google Scholar 

  17. Tang, Y., Xiao, X., Shi, Y.: Influence maximization: near-optimal time complexity meets practical efficiency. In: SIGMOD, pp. 75–86. ACM (2014)

    Google Scholar 

  18. Guo, J., Zhang, P., Zhou, C., Cao, Y., Guo, L.: Personalized influence maximization on social networks. In: CIKM (2013)

    Google Scholar 

  19. Facebook official blog: People You May Know. https://www.facebook.com/notes/facebook/people-you-may-know/15610312130

  20. Chen, J., Geyer, W., Dugan, C., Muller, M., Guy, I.: Make new friends, but keep the old: recommending people on social networking sites. In: SIGCHI (2009)

    Google Scholar 

  21. Guy, I., Ronen, I., Wilcox, E.: Do you know?: recommending people to invite into your social network. In: IUI (2009)

    Google Scholar 

  22. Tian, Y., He, Q., Zhao, Q., Liu, X., Lee, W.c.: Boosting social network connectivity with link revival. In: CIKM (2010)

    Google Scholar 

  23. Saito, K., Kimura, M., Ohara, K., Motoda, H.: Which targets to contact first to maximize influence over social network. In: Greenberg, A.M., Kennedy, W.G., Bos, N.D. (eds.) SBP 2013. LNCS, vol. 7812, pp. 359–367. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  24. Goyal, A., Lakshmanan, L.V.S.: Recmax: exploiting recommender systems for fun and profit. In: KDD (2012)

    Google Scholar 

  25. Khuller, S., Moss, A., Naor, J.: The budgeted maximum coverage problem. Inf. Process. Lett. 70(1), 39–45 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  26. Krause, A., Golovin, D.: Submodular function maximization. Tractability: Practical Approaches to Hard Problems 3, 19 (2012)

    Google Scholar 

  27. Yang, Y., Chen, E., Liu, Q., Xiang, B., Xu, T., Shad, S.A.: On approximation of real-world influence spread. In: Flach, P.A., De Bie, T., Cristianini, N. (eds.) ECML PKDD 2012, Part II. LNCS, vol. 7524, pp. 548–564. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  28. Liu, Q., Xiang, B., Zhang, L., Chen, E., Tan, C., Chen, J.: Linear computation for independent social influence. In: ICDM (2013)

    Google Scholar 

  29. Tong, H., Faloutsos, C., Pan, J.Y.: Random walk with restart: fast solutions and applications. In: KIS (2008)

    Google Scholar 

  30. Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: bringing order to the web (1999)

    Google Scholar 

  31. Goyal, A., Bonchi, F., Lakshmanan, L.V.: A data-based approach to social influence maximization. Proceedings of the VLDB Endowment 5(1), 73–84 (2011)

    Article  Google Scholar 

  32. Anagnostopoulos, A., Kumar, R., Mahdian, M.: Influence and correlation in social networks. In: KDD, pp. 7–15. ACM (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Science and Technology of China, Hefei, Anhui, China

    Guowei Ma, Qi Liu & Enhong Chen

  2. Microsoft, Search Technology Center Asia (STCA), Beijing, China

    Biao Xiang

Authors
  1. Guowei Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qi Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Enhong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Biao Xiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qi Liu .

Editor information

Editors and Affiliations

  1. Google, CA, USA

    Xin Luna Dong

  2. Postdoc Apartments (Hong Lou) 4-1-4, Shandong University, Li Cheng, Jinan, China

    Xiaohui Yu

  3. Tsinghua University, Beijing, China

    Jian Li

  4. Northeastern University, BOSTON, USA

    Yizhou Sun

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Ma, G., Liu, Q., Chen, E., Xiang, B. (2015). Individual Influence Maximization via Link Recommendation. In: Dong, X., Yu, X., Li, J., Sun, Y. (eds) Web-Age Information Management. WAIM 2015. Lecture Notes in Computer Science(), vol 9098. Springer, Cham. https://doi.org/10.1007/978-3-319-21042-1_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-21042-1_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21041-4

  • Online ISBN: 978-3-319-21042-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation