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 \((1-\frac {1}{e})\)-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.
Similar content being viewed by others
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: Theoretical Computer Science, Essays in Memory of Shimon Even, pp. 218–240 (2006)
on Drugs, U.N.O., Crime: The use of the internet for terrorist purposes (2012)
Ghaffari, M., Kuhn, F.: Distributed minimum cut approximation. In: Proceedings of DISC, pp. 1–15 (2013)
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)
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)
Papadimitriou, C.H., Steiglitz, K.: The Max-Flow, Min-Cut theorem. In: Combinatorial Optimization: Algorithms and Complexity, pp. 117–120. Prentice-Hall (1982)
Rodriguez, M.G., Leskovec, J., Balduzzi, D., Schölkopf, B.: Uncovering the structure and temporal dynamics of information propagation. Netw. Sci. 2(01), 26–65 (2014)
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)
Vazirani, V.V.: Approximation algorithms. Springer Science & Business Media (2013)
Wang, S., Zhao, X., Chen, Y., Li, Z., Zhang, K., Xia, J.: Negative influence minimizing by blocking nodes in social networks. In: Proceedings of Late-Breaking Developments in the Field of Artificial Intelligence, pp. 134–136 (2013)
Wang, X., Deng, K., Li, J., Yu, J.X., Jensen, C.S.: Targeted influence minimization in social networks. In: Proceedings of PAKDD (2018)
Yao, Q., Shi, R., Zhou, C., Wang, P., Guo, L.: Topic-aware social influence minimization. In: Proceedings of WWW ’15 Companion, 1, pp. 139–140 (2015)
Acknowledgements
This work is supported by the ARC Discovery Project under grant No. DP160102114.
Author information
Authors and Affiliations
Corresponding authors
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Wang, X., Deng, K., Li, J. et al. Efficient targeted influence minimization in big social networks. World Wide Web 23, 2323–2340 (2020). https://doi.org/10.1007/s11280-019-00748-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11280-019-00748-z