Abstract
An online social network can be used for the diffusion of malicious information like derogatory rumors, disinformation, hate speech, revenge pornography, etc. This motivates the study of influence minimization that aim to prevent the spread of malicious information. Unlike previous influence minimization work, this study considers the influence minimization in relation to a particular group of social network users, called targeted influence minimization. Thus, the objective is to protect a set of users, called target nodes, from malicious information originating from another set of users, called active nodes. This study also addresses two fundamental, but largely ignored, issues in different influence minimization problems: (i) the impact of a budget on the solution; (ii) robust sampling. To this end, two scenarios are investigated, namely unconstrained and constrained budget. Given an unconstrained budget, we provide an optimal solution; Given a constrained budget, we show the problem is NP-hard and develop a greedy algorithm with an \((1-1/e)\)-approximation. More importantly, in order to solve the influence minimization problem in large, real-world social networks, we propose a robust sampling-based solution with a desirable theoretic bound. Extensive experiments using real social network datasets offer insight into the effectiveness and efficiency of the proposed solutions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Borgs, C., Brautbar, M., Chayes, J.T., Lucier, B.: Maximizing social influence in nearly optimal time. In: Proceedings of SODA, pp. 946–957 (2014)
Chen, W., Wang, C., Wang, Y.: Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: Proceedings of ACM SIGKDD, pp. 1029–1038 (2010)
Dinitz, Y.: Dinitz’ algorithm: the original version and Even’s version. In: Goldreich, O., Rosenberg, A.L., Selman, A.L. (eds.) Theoretical Computer Science, Essays in Memory of Shimon Even. LNCS, vol. 3895, pp. 218–240. Springer, Heidelberg (2006). https://doi.org/10.1007/11685654_10
Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of ACM SIGKDD, pp. 137–146 (2003)
Khalil, E., Dilkina, B., Song, L.: CuttingEdge: influence minimization in networks. In: Proceedings of Workshop on Frontiers of Network Analysis: Methods, Models, and Applications at NIPS (2013)
Kimura, M., Saito, K., Motoda, H.: Minimizing the spread of contamination by blocking links in a network. In: Proceedings of AAAI, pp. 1175–1180 (2008)
Kimura, M., Saito, K., Nakano, R.: Extracting influential nodes for information diffusion on a social network. In: Proceedings of AAAI, pp. 1371–1376 (2007)
Li, Y., Zhang, D., Tan, K.: Real-time targeted influence maximization for online advertisements. PVLDB 8(10), 1070–1081 (2015)
Luo, C., Cui, K., Zheng, X., Zeng, D.D.: Time critical disinformation influence minimization in online social networks. In: Proceedings of JISIC, pp. 68–74 (2014)
Papadimitriou, C.H., Steiglitz, K.: The max-flow, min-cut theorem. In: Combinatorial Optimization: Algorithms and Complexity, pp. 117–120. Prentice-Hall (1982)
Shirazipourazad, S., Bogard, B., Vachhani, H., Sen, A., Horn, P.: Influence propagation in adversarial setting: how to defeat competition with least amount of investment. In: Proceedings of ACM SIGMOD, pp. 585–594 (2012)
Song, C., Hsu, W., Lee, M.: Temporal influence blocking: minimizing the effect of misinformation in social networks. In: Proceedings of IEEE ICDE, pp. 847–858 (2017)
Tang, Y., Xiao, X., Shi, Y.: Influence maximization: near-optimal time complexity meets practical efficiency. In: Proceedings of ACM SIGMOD, pp. 75–86 (2014)
Yao, Q., Shi, R., Zhou, C., Wang, P., Guo, L.: Topic-aware social influence minimization. In: Proceedings of WWW 2015 Companion, no. 1, pp. 139–140 (2015)
Acknowledgement
This work is supported by the ARC Discovery Project under grant No. DP160102114.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Wang, X., Deng, K., Li, J., Yu, J.X., Jensen, C.S., Yang, X. (2018). Targeted Influence Minimization in Social Networks. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10939. Springer, Cham. https://doi.org/10.1007/978-3-319-93040-4_54
Download citation
DOI: https://doi.org/10.1007/978-3-319-93040-4_54
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93039-8
Online ISBN: 978-3-319-93040-4
eBook Packages: Computer ScienceComputer Science (R0)