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Activation Probability Maximization for Target Users Under Influence Decay Model

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Computing and Combinatorics (COCOON 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11653))

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Abstract

In this paper, we study how to activate a specific set of targeting users \(\mathcal {T}\), e.g., selling a product to a specific target group, is a practical problem for using the limited budget efficiently. To address this problem, we first propose the Activation Probability Maximization (APM) problem, i.e., to select a seed set S such that the activation probability of the target users in \(\mathcal {T}\) is maximized. Considering that the influence will decay during information propagation, we propose a novel and practical Influence Decay Model (IDM) as the information diffusion model in the APM problem. Based on the IDM, we show that the APM problem is NP-hard and the objective function is monotone non-decreasing and submodular. We provide a (\(1-1/e\))-approximation Basic Greedy Algorithm (BGA). Furthermore, a speed-up Scalable Algorithm (SA) is proposed for online large social networks. Finally, we run our algorithms by simulations on synthetic and real-life social networks to evaluate the effectiveness and efficiency of the proposed algorithms. Experimental results validate our algorithms are superior to the comparison algorithms.

This work is partly supported by National Natural Science Foundation of China under grant 11671400, 61672524.

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Notes

  1. 1.

    The potential value can be obtained through statistical or machine learning based methods. And this is beyond the scope of this paper.

  2. 2.

    In fact, the seeds activating the inactive nodes is a stochastic process.

  3. 3.

    http://snap.stanford.edu/data.

References

  1. 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. ACM (2010)

    Google Scholar 

  2. 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. ACM (2009)

    Google Scholar 

  3. Feng, S., Chen, X., Cong, G., Zeng, Y., Chee, Y.M., Xiang, Y.: Influence maximization with novelty decay in social networks. In: AAAI, pp. 37–43 (2014)

    Google Scholar 

  4. Gao, S., Ma, J., Chen, Z., Wang, G., Xing, C.: Ranking the spreading ability of nodes in complex networks based on local structure. Phys. A Stat. Mech. Appl. 403, 130–147 (2014)

    Article  Google Scholar 

  5. Guo, J., Zhang, P., Zhou, C., Cao, Y., Guo, L.: Personalized influence maximization on social networks. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, pp. 199–208. ACM (2013)

    Google Scholar 

  6. Karp, R.M.: Reducibility among combinatorial problems. In: Miller, R.E., Thatcher, J.W., Bohlinger, J.D. (eds.) Complexity of Computer Computations. The IBM Research Symposia Series, pp. 85–103. Springer, Boston (1972). https://doi.org/10.1007/978-1-4684-2001-2_9

    Chapter  Google Scholar 

  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. Kuhnle, A., Pan, T., Alim, M.A., Thai, M.T.: Scalable bicriteria algorithms for the threshold activation problem in online social networks. In: INFOCOM 2017-IEEE Conference on Computer Communications, pp. 1–9. IEEE (2017)

    Google Scholar 

  9. Liu, B., Cong, G., Xu, D., Zeng, Y.: Time constrained influence maximization in social networks. In: 2012 IEEE 12th International Conference on Data Mining (ICDM), pp. 439–448. IEEE (2012)

    Google Scholar 

  10. Mohammadi, A., Saraee, M., Mirzaei, A.: Time-sensitive influence maximization in social networks. J. Inf. Sci. 41(6), 765–778 (2015)

    Article  Google Scholar 

  11. 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  Google Scholar 

  12. Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: bringing order to the web. Technical report, Stanford InfoLab (1999)

    Google Scholar 

  13. Song, C., Hsu, W., Lee, M.L.: Targeted influence maximization in social networks. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 1683–1692. ACM (2016)

    Google Scholar 

  14. Sun, H., Cheng, R., Xiao, X., Yan, J., Zheng, Y., Qian, Y.: Maximizing social influence for the awareness threshold model. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds.) DASFAA 2018, Part I. LNCS, vol. 10827, pp. 491–510. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91452-7_32

    Chapter  Google Scholar 

  15. Tang, J., Lou, T., Kleinberg, J.: Inferring social ties across heterogenous networks. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, pp. 743–752. ACM (2012)

    Google Scholar 

  16. Tong, G., Wu, W., Tang, S., Du, D.Z.: Adaptive influence maximization in dynamic social networks. IEEE/ACM Trans. Netw. (TON) 25(1), 112–125 (2017)

    Article  Google Scholar 

  17. Wang, B., Chen, G., Fu, L., Song, L., Wang, X., Liu, X.: DRIMUX: dynamic rumor influence minimization with user experience in social networks. In: AAAI, vol. 16, pp. 791–797 (2016)

    Google Scholar 

  18. Yan, Q., Huang, H., Gao, Y., Lu, W., He, Q.: Group-level influence maximization with budget constraint. In: Candan, S., Chen, L., Pedersen, T.B., Chang, L., Hua, W. (eds.) DASFAA 2017, Part I. LNCS, vol. 10177, pp. 625–641. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55753-3_39

    Chapter  Google Scholar 

  19. Yang, D.N., Hung, H.J., Lee, W.C., Chen, W.: Maximizing acceptance probability for active friending in online social networks. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 713–721. ACM (2013)

    Google Scholar 

  20. Zhou, C., Guo, L.: A note on influence maximization in social networks from local to global and beyond. Procedia Comput. Sci. 30, 81–87 (2014)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Information, Renmin University of China, Beijing, China

    Ruidong Yan, Deying Li & Yongcai Wang

  2. Department of Computer Science, University of Texas at Dallas, Richardson, USA

    Yi Li

  3. Department of Computer Science, California State University at Los Angeles, Los Angeles, USA

    Yuqing Zhu

  4. Department of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China

    Hongwei Du

Authors
  1. Ruidong Yan
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  2. Yi Li
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  3. Deying Li
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  4. Yuqing Zhu
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  5. Yongcai Wang
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  6. Hongwei Du
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Corresponding author

Correspondence to Deying Li .

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Editors and Affiliations

  1. The University of Texas at Dallas, Richardson, TX, USA

    Ding-Zhu Du

  2. Xidian University, Xi'an, China

    Zhenhua Duan

  3. Xidian University, Xi'an, China

    Cong Tian

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Yan, R., Li, Y., Li, D., Zhu, Y., Wang, Y., Du, H. (2019). Activation Probability Maximization for Target Users Under Influence Decay Model. In: Du, DZ., Duan, Z., Tian, C. (eds) Computing and Combinatorics. COCOON 2019. Lecture Notes in Computer Science(), vol 11653. Springer, Cham. https://doi.org/10.1007/978-3-030-26176-4_50

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  • DOI: https://doi.org/10.1007/978-3-030-26176-4_50

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26175-7

  • Online ISBN: 978-3-030-26176-4

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