Abstract
In recent years, with the rapid development of Internet technology, social networks such as Facebook, Twitter and Google+ have been integrated into daily life. These social networks not only help users stay in touch with family and friends, but also keep abreast of breaking news and emerging contents. However, in some scenarios, we need to take measures to control or limit the spread of negative information such as rumors. In this paper, we first propose the Minimizing Influence of Rumor (MIR) problem, i.e., selecting a blocker set \(\mathcal {B}\) with k nodes such that the users’ total activation probability from rumor source S is minimized on the network. Then we use classical Independent Cascade (IC) model as information diffusion model. Based on this model, we prove that the objective function is monotone decreasing and non-submodular. In order to solve MIR problem effectively, we propose a two-stages method named GCSSB that includes Generating Candidate Set and Selecting Blockers stages. Finally, we evaluate proposed method by simulations on synthetic and real-life social networks. Furthermore, we also compare with other heuristic methods such as Out-Degree, Betweenness Centrality and PageRank. Experimental results show that our method is superior to comparison approaches.
This work is partly supported by National Natural Science Foundation of China under grant 11671400 and National Science Foundation under grant 1747818.
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Self-loops and multiple edges are not allowed.
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Yan, R., Li, D., Wu, W., Du, DZ. (2018). Minimizing Influence of Rumors by Blockers on Social Networks. In: Chen, X., Sen, A., Li, W., Thai, M. (eds) Computational Data and Social Networks. CSoNet 2018. Lecture Notes in Computer Science(), vol 11280. Springer, Cham. https://doi.org/10.1007/978-3-030-04648-4_1
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DOI: https://doi.org/10.1007/978-3-030-04648-4_1
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