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2019 | OriginalPaper | Chapter

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

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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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Literature
1.
go back to reference 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) 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)
2.
go back to reference Chen, W., Lu, W., Zhang, N.: Time-critical influence maximization in social networks with time-delayed diffusion process. arXiv preprint arXiv:1204.3074 (2012) 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.
go back to reference 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) 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)
4.
go back to reference 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) 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)
5.
go back to reference 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) 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)
6.
go back to reference 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) 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)
7.
go back to reference 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) 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)
8.
go back to reference 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) 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)
9.
go back to reference 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) 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)
10.
go back to reference 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) 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)
11.
go back to reference 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) 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)
12.
go back to reference 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) 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)
13.
go back to reference 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) 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)
14.
go back to reference Khuller, S., Moss, A., Naor, J.S.: The budgeted maximum coverage problem. Inf. Process. Lett. 70(1), 39–45 (1999)MathSciNetCrossRef Khuller, S., Moss, A., Naor, J.S.: The budgeted maximum coverage problem. Inf. Process. Lett. 70(1), 39–45 (1999)MathSciNetCrossRef
16.
go back to reference 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) 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)
17.
go back to reference 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) 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)
18.
go back to reference Nguyen, H., Zheng, R.: On budgeted influence maximization in social networks. IEEE J. Sel. Areas Commun. 31(6), 1084–1094 (2013)CrossRef Nguyen, H., Zheng, R.: On budgeted influence maximization in social networks. IEEE J. Sel. Areas Commun. 31(6), 1084–1094 (2013)CrossRef
20.
go back to reference 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) 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)
21.
go back to reference 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)CrossRef 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)CrossRef
22.
go back to reference 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) 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)
23.
go back to reference 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) 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)
Metadata
Title
Cost-Aware Targeted Viral Marketing with Time Constraints in Social Networks
Authors
Ke Xu
Kai Han
Copyright Year
2019
DOI
https://doi.org/10.1007/978-3-030-12981-1_5