Abstract
In this paper, we address the problem of minimizing the negative influence of undesirable things by blocking a limited number of links in a network. When undesirable thing such as a rumor or an infection emerges in a social network and part of users have already been infected, our goal is to minimize the size of ultimately infected users by blocking k links. A greedy algorithm with accuracy guarantee and two efficient heuristics for finding approximate solutions to this problem are proposed. Using two real networks, we demonstrate experimentally that the greedy algorithm is more effective in terms of minimizing negative influence, while the heuristics based on betweenness and out-degree are orders of magnitude faster than the greedy algorithm in terms of running time.
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Acknowledgments
This work was supported by the Strategic Leading Science and Technology Projects of Chinese Academy of Sciences (No. XDA06030200).
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Yao, Q., Zhou, C., Xiang, L., Cao, Y., Guo, L. (2015). Minimizing the Negative Influence by Blocking Links in Social Networks. In: Yueming, L., Xu, W., Xi, Z. (eds) Trustworthy Computing and Services. ISCTCS 2014. Communications in Computer and Information Science, vol 520. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47401-3_9
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DOI: https://doi.org/10.1007/978-3-662-47401-3_9
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