Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

A community-based algorithm for influence blocking maximization in social networks

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

With the increasing popularity of social networking sites and the convenience of information diffusion in social network, social network has been a huge platform for information diffusion and knowledge sharing. However, the incapability of the supervision over the content of networks usually leads to the threats of negative influence, which may lead to undesirable effects. Influence blocking maximization (IBM) problem which aims to find a subset of nodes that need to adopt the positive influence (L) to minimize the number of nodes that adopt the negative influence (C) at the end of both propagation processes is addressed in this work. Under a well-known Campaign-Oblivious Independent Cascade Model, the objective function of IBM is submodular, and thus an approximation algorithm Greedy is obtained. Subsequently, based on the locality of influence diffusion in social networks, an efficient algorithm CB_IBM is proposed, which is based on the community structure of the network. Extensive simulations of CB_IBM, Greedy, and other baseline algorithms have been conducted on two real-world datasets, and experiments show that, in terms of the blocking effect, CB_IBM consistently matches the performance of Greedy, however, it is much faster than Greedy.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 57–66, August 26–29 (2001)

  2. Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 61–70, July 23–25 (2002)

  3. Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146, August 24–27 (2003)

  4. Liu, B., Cong, G., Xu, D., Zeng, Y.: Time constrained influence maximization in social networks. In: Proceedings of the IEEE 12th International Conference on Data Mining, Brussels, Belgium, pp. 439–448, December 10–13 (2012)

  5. Chen, W., Wang, C., Wang, Y.: Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1029–1038, July 25–28 (2010)

  6. 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, June 28–July 1 (2009)

  7. Chen, W., Yuan, Y., Zhang, L.: Scalable influence maximization in social networks under the linear threshold model. In: Proceedings of the IEEE 10th International Conference on Data Mining, pp. 88–97, December 13–17 (2010)

  8. Narayanam, R., Narahari, Y.: A shapley value-based approach to discover influential nodes in social networks. IEEE Trans. Autom. Sci. Eng. 8(1), 130–147 (2011)

    Article  Google Scholar 

  9. 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, August 12–15 (2007)

  10. Goyal, A., Lu, W., Lakshmanan, L.V.S.: CELF++: optimizing the greedy algorithm for influence maximization in social networks. In: Proceedings of the 20th international conference companion on World Wide Web, pp. 47–48, March 28–April 1 (2011)

  11. Wang, Y., Cong, G., Song, G., Xie, K.: Community-based greedy algorithm for mining top-K influential nodes in mobile social networks. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1039–1048 (2010)

  12. Shirazipourazad, S., Bogard, B., Vachhani, H., et al.: Influence propagation in adversarial setting: how to defeat competition with least amount of investment. In: Proceedings of the 21st ACM international conference on Information and knowledge Management (CIKM’ 2012), pp. 585–594 (2012)

  13. Yu, Y., Berger-Wolf, T.Y., Saia J.: Finding spread blockers in dynamic networks. In: Advances in Social Network Mining and Analysis, pp. 55–76. Springer, Berlin (2010)

  14. Nguyen, N.P., Yan, G., Thai, M.T. et al.: Containment of misinformation spread in online social networks. In: Proceedings of the 3rd Annual ACM Web Science Conference, pp. 213–222 (2012)

  15. Budak, C., Agrawal, D., El Abbadi A.: Limiting the spread of misinformation in social networks. In: Proceedings of the 20th international conference on World Wide Web, pp. 665–674 (2011)

  16. He, X., Song, G., Chen, W., et al.: Influence blocking maximization in social networks under the competitive linear threshold model. In: Proceedings of the 2012 SIAM International Conference on Data Mining, pp. 463–474 (2012)

  17. Alon, N., Feldman, M., Procaccia, A.D., et al.: A note on competitive diffusion through social networks. Inf. Process. Lett. 110(6), 221–225 (2010)

    Article  MathSciNet  Google Scholar 

  18. Apt, K.R., Simon, S.E.: Choosing products in social networks. In: Proceedings of the International Workshop on Internet and Network Economics (WINE), pp. 100–113 (2012)

  19. Borodin, A., Braverman, M., Lucier, B., et al.: Strategyproof mechanisms for competitive influence in networks. In: Proceedings of 20th international conference on World Wide Web, pp. 141–150 (2013)

  20. Borodin, A., Filmus, Y., Oren J.: Threshold models for competitive influence in social networks. In: WINE, vol. 6484, pp. 539–550(2010)

    Google Scholar 

  21. Wu, H., Liu, W., Yue, K., Huang, W., Yang, K.: Maximizing the spread of competitive influence in a social network oriented to viral marketing. In: Proceedings of the 16th International conference on Web-Age Information Management(WAIM2015), pp. 516–519 (2015)

    Chapter  Google Scholar 

  22. Liu, W., Yue, K., Wu, H., Li, J., Liu, D., Tang, D.: Containment of competitive influence spread in social networks. Knowl.-Based Syst. 109, 266–275 (2016)

    Article  Google Scholar 

  23. Zhu, Y., Li, D., Zhang, Z.: Minimum cost seed set for competitive social influence. In: Proceedings of the IEEE International Conference on Computer Communications (IEEE INFOCOM2016), pp. 1–9 (2016)

  24. Wu, H., Liu, W., Yue, K., Li, J., Huang, W.: Selecting seeds for competitive influence spread maximization in social networks. In: Proceedings of the International Conference of Young Computer Scientists, Engineers and Educators (ICYCSEE 2016), pp. 600–611 (2016)

    Google Scholar 

  25. Lin, Y., Lui, J.: Algorithmic design for competitive influence maximization problems. Eprint Arxiv (2014)

  26. Lin, Y., Lui, J.C.S.: Analyzing competitive influence maximization problems with partial information: an approximation algorithmic framework. Perform. Eval. 91, 187–204 (2015)

    Article  Google Scholar 

  27. Masucci, A.M., Silva, A.: Strategic resource allocation for competitive influence in social networks. In: Proceedings of the 52nd Annual Allerton Conference on Communication, Control, and Computing, pp. 951–958 (2014)

  28. Carnes, T., Nagarajan, C., Wild, S.M. et al.: Maximizing influence in a competitive social network: a follower’s perspective. In: Proceedings of 9th international conference on Electronic Commerce, pp. 351–360 (2007)

  29. Lancichinetti, A., Fortunato, S., Radicchi, F.: Banchmark Graph for testing community detection algorithms. Phys. Rev. E 78(4), 046110 (2008)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Foundation Science (No. 61472340, No. 61702445), Shandong province colleges and universities science and technology research Project (No. J15LN81), and the doctor Foundation of Zaozhuang university (No.1020703).

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Zaozhuang University, Zaozhuang, 277100, Shandong, China

    Jiaguo Lv, Bin Yang, Zhen Yang & Wei Zhang

Authors
  1. Jiaguo Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bin Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhen Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jiaguo Lv.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lv, J., Yang, B., Yang, Z. et al. A community-based algorithm for influence blocking maximization in social networks. Cluster Comput 22 (Suppl 3), 5587–5602 (2019). https://doi.org/10.1007/s10586-017-1390-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-1390-6

Keywords

Search

Navigation