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Proactive rumor control in online networks

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Abstract

The spread of rumors through online networks not only threatens the public safety but also results in loss of financial property. To serve as a reliable platform for spreading critical information, many work study the problem of rumor control which aims at limiting the pernicious influence of rumors. These methods, however, only assume that users are passive receivers of rumors even if the users can browse the rumors on their own. To overcome this issue, in this paper we study the rumor spread from a proactive perspective and introduce a novel rumor control problem, called users’ B rowsing based rU mor blocK (BUK). Given a rumor set R, BUK can be summarized as targeting k nodes as ‘protectors’ to save nodes in G from being influenced by R as many as possible. Different with the previous studies, BUK considers that the rumors spread via users’ browsing behaviors, and models the propagation based on the random walk model. Theoretical analysis shows that the problem of BUK is submodular, and we propose two greedy algorithms that can approximate BUK within a ratio of (1 − 1/e). However, both of them consume high spaces and thereby cannot be applied to very large networks. Therefore, we further propose a ranking based method RanSel to solve BUK heuristically, which only consumes a linear space to the graph size. The experiments reveal that the effectiveness of our methods outperforms the baseline by 6% to 59.2%, and our methods can achieve such an effective result in reasonable time.

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Notes

  1. http://www.dailymail.co.uk/sciencetech/article-3090221/

  2. Note that, G should either be undirected or directed. When G is undirected, it means that the adjacent users can exchange the information in mutual ways.

  3. As the number of non-zero entries in Q is O(m), we can compute the multiplication as the manner of the pageRank algorithm [20] to reduce the computation cost.

References

  1. Albert, R., Jeong, H., Barabási, A.L.: Error and attack tolerance of complex networks. Nature 406(6794), 378 (2000)

    Article  Google Scholar 

  2. Allcott, H., Gentzkow, M.: Social media and fake news in the 2016 election. J. Econ. Perspect. 31(2), 211–36 (2017)

    Article  Google Scholar 

  3. Bharathi, S., Kempe, D., Salek, M.: Competitive influence maximization in social networks. In: WINE, pp. 306–311. Springer (2007)

  4. Borgs, C., Brautbar, M., Chayes, J., Lucier, B.: Maximizing social influence in nearly optimal time. In: SODA, pp. 946–957. SIAM (2014)

  5. Borodin, A., Filmus, Y., Oren, J.: Threshold models for competitive influence in social networks. In: International Workshop on Internet and Network Economics, pp. 539–550. Springer (2010)

  6. Carnes, T., Nagarajan, C., Wild, S.M., Van Zuylen, A.: Maximizing influence in a competitive social network: A follower’s perspective. In: ICEC, pp. 351–360. ACM (2007)

  7. Chen, W., Wang, C., Wang, Y.: Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: SIGKDD, pp. 1029–1038. ACM (2010)

  8. Coppersmith, D., Winograd, S.: Matrix multiplication via arithmetic progressions. In: STOC, pp. 1–6. ACM (1987)

  9. Cormen, T.H.: Introduction to Algorithms. MIT Press (2009)

  10. He, J., Liang, H., Yuan, H.: Controlling infection by blocking nodes and links simultaneously. In: WINE, pp. 206–217. Springer (2011)

  11. He, X., Song, G., Chen, W., Jiang, Q.: Influence blocking maximization in social networks under the competitive linear threshold model. In: SDM, pp. 463–474. SIAM (2012)

  12. Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: SIGKDD, pp. 137–146. ACM (2003)

  13. 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)

  14. Korkmaz, G., Kuhlman, C.J., Ravi, S., Vega-Redondo, F.: Spreading of social contagions without key players. World Wide Web, 1–35 (2017)

  15. Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., Glance, N.: Cost-effective outbreak detection in networks. In: SIGKDD, pp. 420–429. ACM (2007)

  16. Li, H., Bhowmick, S.S., Cui, J., Gao, Y., Ma, J.: Getreal: Towards realistic selection of influence maximization strategies in competitive networks. In: SIGMOD, pp. 1525–1537. ACM (2015)

  17. Liu, X., Li, S., Liao, X., Peng, S., Wang, L., Kong, Z.: Know by a handful the whole sack: Efficient sampling for top-k influential user identification in large graphs. World Wide Web 17(4), 627–647 (2014)

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  19. Newman, M.E., Forrest, S., Balthrop, J.: Email networks and the spread of computer viruses. Phys. Rev. E 66(3), 035101 (2002)

    Article  Google Scholar 

  20. Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the Web. Technical Report (1999)

  21. Spitzer, F.: Principles of random walk, vol. 34. Springer Science & Business Media (2013)

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

  23. Tripathy, R.M., Bagchi, A., Mehta, S.: A study of rumor control strategies on social networks. In: CIKM, pp. 1817–1820. ACM (2010)

  24. Yan, L., Zheng, W., Zhang, H., Tao, H., He, M.: Learning discriminative sentiment chunk vectors for twitter sentiment analysis 18, 1605–1613 (2017)

    Google Scholar 

  25. Yan, L., Yuhui, Z., Cao, J.: Few-shot learning for short text classification. https://doi.org/10.1007/s11042-018-5772-4 (2018)

  26. Yu, Y., Berger-Wolf, T.Y., Saia, J., et al.: Finding spread blockers in dynamic networks. In: ASONAM, pp. 55–76. Springer (2010)

  27. Zhang, H., Zhang, H., Li, X., Thai, M.T.: Limiting the spread of misinformation while effectively raising awareness in social networks. In: CSoNet, pp. 35–47. Springer (2015)

  28. Zhou, Z., Wang, Y., Wu, Q.M.J., Yang, C.N., Sun, X.: Effective and efficient global context verification for image copy detection. Trans. Info. For. Sec. 12(1), 48–63 (2017). https://doi.org/10.1109/TIFS.2016.2601065

    Article  Google Scholar 

  29. Zhou, Z., Wu, Q.J., Huang, F., Sun, X.: Fast and accurate near-duplicate image elimination for visual sensor networks. Int. J. Distrib. Sensor Netw. 13(2), 1550147717694172 (2017). https://doi.org/10.1177/1550147717694172

    Article  Google Scholar 

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Acknowledgments

This work is partially supported by the Ministry of Science and Technology of China, National Key Research and Development Program (2016YFB1000700), ARC (DP170102726, DP180102050), NSF of China (Project Number: 61728204, 91646204, 71603252).

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Authors and Affiliations

  1. Wuhan University, Wuhan, China

    Ping Zhang, Yudong Niu, Songsong Mo & Zhiyong Peng

  2. RMIT University, Melbourne, Australia

    Zhifeng Bao & Yipeng Zhang

  3. Huazhong University of Science and Technology, Wuhan, China

    Fei Geng

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  1. Ping Zhang
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  2. Zhifeng Bao
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  3. Yudong Niu
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  4. Yipeng Zhang
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Correspondence to Zhiyong Peng.

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Zhang, P., Bao, Z., Niu, Y. et al. Proactive rumor control in online networks. World Wide Web 22, 1799–1818 (2019). https://doi.org/10.1007/s11280-018-0623-9

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