Abstract
Social influence problems, such as Influence Maximization (IM), have been widely studied. But a key challenge remains: How does a company select a small size seed set such that the acceptance probability of target users is maximized? In this paper, we first propose the Acceptance Probability Maximization (APM) problem, i.e., selecting a small size seed set S such that the acceptance probability of target users T is maximized. Then we use classical Independent Cascade (IC) model as basic information diffusion model. Based on this model, we prove that APM is NP-hard and the objective function is monotone non-decreasing as well as submodular. Considering community structure of social networks, we transform APM to Maximum Weight Hitting Set (MWHS) problem. Next, we develop a pipage rounding algorithm whose approximation ratio is (\(1-1/e\)). Finally, we evaluate our algorithms by simulations on real-life social networks. Experimental results validate the performance of the proposed algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Notice that \(1-(1-p)^{|U|}\) is maximum probability of T under the IC model.
- 2.
- 3.
We uniformly at random select a probability from {0.1, 0.3, 0.5}.
References
Algesheimer, R., Dholakia, U.M., Herrmann, A.: The social influence of brand community: evidence from European car clubs. J. Mark. 69(3), 19–34 (2005)
Chang, C.W., Yeh, M.Y., Chuang, K.T.: On influence maximization to target users in the presence of multiple acceptances. In: 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 1592–1593. IEEE (2015)
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)
Crandall, D., Cosley, D., Huttenlocher, D., Kleinberg, J., Suri, S.: Feedback effects between similarity and social influence in online communities. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 160–168. ACM (2008)
Du, D.Z., Ko, K.I., Hu, X.: Design and Analysis of Approximation Algorithms, vol. 62. Springer, New York (2011). https://doi.org/10.1007/978-1-4614-1701-9
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 and Knowledge Management, pp. 199–208. ACM (2013)
Karp, R.: Reducibility among combinatorial problems. In: Miller, R.E., Thatcher, J.W., Bohlinger, J.D. (eds.) Complexity of Computer Computations, pp. 85–103. Springer, Boston (1972). https://doi.org/10.1007/978-1-4684-2001-2_9
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)
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)
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)
Temitope, O.A.S., Ahmad, R., Mahmudin, M.: Influence maximization towards target users on social networks for information diffusion. J. Telecommun. Electron. Comput. Eng. (JTEC) 10(1–10), 17–24 (2018)
Valiant, L.G.: The complexity of enumeration and reliability problems. SIAM J. Comput. 8(3), 410–421 (1979)
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)
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)
Acknowledgments
This work was supported in part by the National Natural Science Foundation of China Grant No. 11671400, 61672524. The Fundamental Research Funds for the Central University, and the Research Funds of Renmin University of China, 2015030273, and the Research Funds of Renmin University of China 16XNH116.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Yan, R., Zhu, Y., Li, D., Wang, Y. (2018). Community-Based Acceptance Probability Maximization for Target Users on Social Networks. In: Tang, S., Du, DZ., Woodruff, D., Butenko, S. (eds) Algorithmic Aspects in Information and Management. AAIM 2018. Lecture Notes in Computer Science(), vol 11343. Springer, Cham. https://doi.org/10.1007/978-3-030-04618-7_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-04618-7_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04617-0
Online ISBN: 978-3-030-04618-7
eBook Packages: Computer ScienceComputer Science (R0)