Minimizing Influence of Rumors by Blockers on Social Networks

  • Ruidong Yan
  • Deying LiEmail author
  • Weili Wu
  • Ding-Zhu Du
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11280)


In recent years, with the rapid development of Internet technology, social networks such as Facebook, Twitter and Google+ have been integrated into daily life. These social networks not only help users stay in touch with family and friends, but also keep abreast of breaking news and emerging contents. However, in some scenarios, we need to take measures to control or limit the spread of negative information such as rumors. In this paper, we first propose the Minimizing Influence of Rumor (MIR) problem, i.e., selecting a blocker set \(\mathcal {B}\) with k nodes such that the users’ total activation probability from rumor source S is minimized on the network. Then we use classical Independent Cascade (IC) model as information diffusion model. Based on this model, we prove that the objective function is monotone decreasing and non-submodular. In order to solve MIR problem effectively, we propose a two-stages method named GCSSB that includes Generating Candidate Set and Selecting Blockers stages. Finally, we evaluate proposed method by simulations on synthetic and real-life social networks. Furthermore, we also compare with other heuristic methods such as Out-Degree, Betweenness Centrality and PageRank. Experimental results show that our method is superior to comparison approaches.


Social influence Rumor blocking Social network Submodularity Greedy algorithm 


  1. 1.
    Brandes, U.: On variants of shortest-path betweenness centrality and their generic computation. Soc. Netw. 30(2), 136–145 (2008)CrossRefGoogle Scholar
  2. 2.
    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. ACM (2011)Google Scholar
  3. 3.
    Doerr, B., Fouz, M., Friedrich, T.: Why rumors spread so quickly in social networks. Commun. ACM 55(6), 70–75 (2012)CrossRefGoogle Scholar
  4. 4.
    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
  5. 5.
    ERDdS, P., R&WI, A.: On random graphs i. Publ. Math. Debrecen 6, 290–297 (1959)Google Scholar
  6. 6.
    Fan, L., Lu, Z., Wu, W., Thuraisingham, B., Ma, H., Bi, Y.: Least cost rumor blocking in social networks. In: 2013 IEEE 33rd International Conference on Distributed Computing Systems (ICDCS), pp. 540–549. IEEE (2013)Google Scholar
  7. 7.
    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
  8. 8.
    Khalil, E.B., Dilkina, B., Song, L.: Scalable diffusion-aware optimization of network topology. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1226–1235. ACM (2014)Google Scholar
  9. 9.
    Kimura, M., Saito, K., Motoda, H.: Minimizing the spread of contamination by blocking links in a network. In: AAAI, vol. 8, pp. 1175–1180 (2008)Google Scholar
  10. 10.
    Ma, L.L., Ma, C., Zhang, H.F., Wang, B.H.: Identifying influential spreaders in complex networks based on gravity formula. Phys. A Stat. Mech. Appl. 451, 205–212 (2016)CrossRefGoogle Scholar
  11. 11.
    Page, L., Brin, S., Motwani, R., Winograd, T., et al.: The PageRank citation ranking: bringing order to the web (1998)Google Scholar
  12. 12.
    Tong, G., et al.: An efficient randomized algorithm for rumor blocking in online social networks. IEEE Trans. Netw. Sci. Eng. (2017)Google Scholar
  13. 13.
    Tong, H., Prakash, B.A., Eliassi-Rad, T., Faloutsos, M., Faloutsos, C.: Gelling, and melting, large graphs by edge manipulation. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 245–254. ACM (2012)Google Scholar
  14. 14.
    Wang, S., Zhao, X., Chen, Y., Li, Z., Zhang, K., Xia, J.: Negative influence minimizing by blocking nodes in social networks. In: AAAI (Late-Breaking Developments), pp. 134–136 (2013)Google Scholar
  15. 15.
    Wen, S., Haghighi, M.S., Chen, C., Xiang, Y., Zhou, W., Jia, W.: A sword with two edges: propagation studies on both positive and negative information in online social networks. IEEE Trans. Comput. 64(3), 640–653 (2015)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Yan, R., Zhu, Y., Li, D., Ye, Z.: Minimum cost seed set for threshold influence problem under competitive models. World Wide Web 1–20 (2018)Google Scholar
  17. 17.
    Zhu, Y., Li, D., Zhang, Z.: Minimum cost seed set for competitive social influence. In: IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications, pp. 1–9. IEEE (2016)Google Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of InformationRenmin University of ChinaBeijingChina
  2. 2.Department of Computer ScienceUniversity of Texas at DallasRichardsonUSA

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