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Cost-Aware Targeted Viral Marketing with Time Constraints in Social Networks

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Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2018)

Abstract

Online social networks have been one of the most effective platforms for marketing which is called viral marketing. The main challenge of viral marketing is to seek a set of k users that can maximize the expected influence, which is known as Influence Maximization (IM) problem. In this paper, we incorporate heterogeneous costs and benefits of users and time constraints, including time delay and time deadline of influence diffusion, in IM problem and propose Cost-aware Targeted Viral Marketing with Time constraints (CTVMT) problem to find the most cost-effective seed users who can influence the most relevant users within a time deadline. We study the problem under IC-M and LT-M diffusion model which extends IC and LT model with time constraints. Since CTVMT is NP-hard under two models, we design a BCT-M algorithm using two new benefit sampling algorithms designed for IC-M and LT-M respectively to get a solution with an approximation ratio. To the best of our knowledge, this is the first algorithm that can provide approximation guarantee for our problem. Our empirical study over several real-world networks demonstrates the performances of our proposed solutions.

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References

  1. 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 

  2. Chen, W., Lu, W., Zhang, N.: Time-critical influence maximization in social networks with time-delayed diffusion process. arXiv preprint arXiv:1204.3074 (2012)

  3. Nguyen, H.T., Dinh, T.N., Thai, M.T.: Cost-aware targeted viral marketing in billion-scale networks. In: INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications, pp. 1–9. IEEE (2016)

    Google Scholar 

  4. 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 

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

    Google Scholar 

  6. Jung, K., Heo, W., Chen, W.: IRIE: scalable and robust influence maximization in social networks. In: 2012 IEEE 12th International Conference on Data Mining (ICDM), pp. 918–923. IEEE (2012)

    Google Scholar 

  7. Ohsaka, N., Akiba, T., Yoshida, Y., Kawarabayashi, K.-I.: Fast and accurate influence maximization on large networks with pruned Monte-Carlo simulations. In: AAAI, pp. 138–144 (2014)

    Google Scholar 

  8. Goyal, A., Lu, W., Lakshmanan, L.V.S.: SIMPATH: an efficient algorithm for influence maximization under the linear threshold model. In: 2011 IEEE 11th International Conference on Data Mining (ICDM), pp. 211–220. IEEE (2011)

    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. ACM (2007)

    Google Scholar 

  10. 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 

  11. Borgs, C., Brautbar, M., Chayes, J., Lucier, B.: Maximizing social influence in nearly optimal time. In: Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 946–957. SIAM (2014)

    Google Scholar 

  12. Tang, Y., Xiao, X., Shi, Y.: Influence maximization: near-optimal time complexity meets practical efficiency. In Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, pp. 75–86. ACM (2014)

    Google Scholar 

  13. 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 

  14. Khuller, S., Moss, A., Naor, J.S.: The budgeted maximum coverage problem. Inf. Process. Lett. 70(1), 39–45 (1999)

    Article  MathSciNet  Google Scholar 

  15. Leskovec, J., Krevl, A.: SNAP datasets: stanford large network dataset collection, June 2014. http://snap.stanford.edu/data

  16. Goyal, A., Bonchi, F., Lakshmanan, L.V.S.: Learning influence probabilities in social networks. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 241–250. ACM (2010)

    Google Scholar 

  17. Nguyen, H.T., Thai, M.T., Dinh, T.N.: Stop-and-stare: optimal sampling algorithms for viral marketing in billion-scale networks. In Proceedings of the 2016 International Conference on Management of Data, pp. 695–710. ACM (2016)

    Google Scholar 

  18. Nguyen, H., Zheng, R.: On budgeted influence maximization in social networks. IEEE J. Sel. Areas Commun. 31(6), 1084–1094 (2013)

    Article  Google Scholar 

  19. Ohsaka, N., Yamaguchi, Y., Kakimura, N., Kawarabayashi, K.-I.: Maximizing time-decaying influence in social networks. In: Frasconi, P., Landwehr, N., Manco, G., Vreeken, J. (eds.) ECML PKDD 2016. LNCS (LNAI), vol. 9851, pp. 132–147. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46128-1_9

    Chapter  Google Scholar 

  20. Mossel, E., Roch, S.: On the submodularity of influence in social networks. In: Proceedings of the Thirty-Ninth Annual ACM Symposium on Theory of Computing, pp. 128–134. ACM (2007)

    Google Scholar 

  21. Nguyen, H.T., Nguyen, T.P., Vu, T.N., Dinh, T.N.: Outward influence and cascade size estimation in billion-scale networks. Proc. ACM Meas. Anal. Comput. Syst. 1(1), 20 (2017)

    Article  Google Scholar 

  22. Tang, Y., Shi, Y., Xiao, X.: Influence maximization in near-linear time: a martingale approach. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, pp. 1539–1554. ACM (2015)

    Google Scholar 

  23. 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 

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Acknowledgements

This work is partially supported by National Natural Science Foundation of China (NSFC) under Grant No. 61772491, No. 61472460, and Natural Science Foundation of Jiangsu Province under Grant No. BK20161256, and Anhui Initiative in Quantum Information Technologies AHY150300.

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Authors and Affiliations

  1. School of Computer Science and Technology, Suzhou Institute for Advanced Study, University of Science and Technology of China, Hefei, People’s Republic of China

    Ke Xu & Kai Han

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  1. Ke Xu
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  2. Kai Han
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Correspondence to Kai Han .

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Editors and Affiliations

  1. Shanghai University, Shanghai, China

    Honghao Gao

  2. University of West London, London, UK

    Xinheng Wang

  3. Hangzhou Dianzi University, Hangzhou Shi, Zhejiang, China

    Yuyu Yin

  4. London South Bank University, London, UK

    Muddesar Iqbal

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Xu, K., Han, K. (2019). Cost-Aware Targeted Viral Marketing with Time Constraints in Social Networks. In: Gao, H., Wang, X., Yin, Y., Iqbal, M. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 268. Springer, Cham. https://doi.org/10.1007/978-3-030-12981-1_5

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  • DOI: https://doi.org/10.1007/978-3-030-12981-1_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-12980-4

  • Online ISBN: 978-3-030-12981-1

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