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Cluster Computing

, Volume 22, Supplement 3, pp 5587–5602 | Cite as

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

  • Jiaguo LvEmail author
  • Bin Yang
  • Zhen Yang
  • Wei Zhang
Article

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.

Keywords

Influence blocking maximization Competitive influence diffusion Community structure Social networks 

Notes

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).

References

  1. 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)Google Scholar
  2. 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)Google Scholar
  3. 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)Google Scholar
  4. 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)Google Scholar
  5. 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)Google Scholar
  6. 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)Google Scholar
  7. 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)Google Scholar
  8. 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)CrossRefGoogle Scholar
  9. 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)Google Scholar
  10. 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)Google Scholar
  11. 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)Google Scholar
  12. 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)Google Scholar
  13. 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)Google Scholar
  14. 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)Google Scholar
  15. 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)Google Scholar
  16. 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)Google Scholar
  17. 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)MathSciNetCrossRefGoogle Scholar
  18. 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)Google Scholar
  19. 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)Google Scholar
  20. 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. 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)CrossRefGoogle Scholar
  22. 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)CrossRefGoogle Scholar
  23. 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)Google Scholar
  24. 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. 25.
    Lin, Y., Lui, J.: Algorithmic design for competitive influence maximization problems. Eprint Arxiv (2014)Google Scholar
  26. 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)CrossRefGoogle Scholar
  27. 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)Google Scholar
  28. 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)Google Scholar
  29. 29.
    Lancichinetti, A., Fortunato, S., Radicchi, F.: Banchmark Graph for testing community detection algorithms. Phys. Rev. E 78(4), 046110 (2008)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.School of Information Science and EngineeringZaozhuang UniversityZaozhuangChina

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