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| Uncertain Influence Sources Oriented Influence Blocking Maximization in Social Networks |
| LI Jin①② YUE Kun③ YOU Jie①② XIE Xiaorui①② ZHANG Yunfei③ |
①(School of Software, Yunnan University, Kunming 650500, China)
②(Key Laboratory of Software Engineering, Yunnan Province, Kunming 650500, China)
③(School of Information Science and Engineering, Yunnan University, Kunming 650500, China) |
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Abstract Influence blocking maximization is currently a focused issue in the research area of social networks. This paper considers the issue of influence blocking maximization with uncertain negative influence sources. First, in order to increase efficiency of blocking seeds mining algorithms, the approximate estimation method of influence propagation of negative seeds under the competitive linear threshold model is discussed. Based on the estimation, a blocking seeds mining algorithm for finite uncertain negatively influence sources is proposed to maximize expected influence blocking utility. Second, for the case of huge amount of negatively influence sources with uncertainty, a blocking seeds mining algorithm based on the sampling average approximation approach is proposed to balance the tradeoffs between scalability and effectiveness of the influence blocking maximization. Finally, experiments are carried on real data sets of social networks to verify the feasibility and scalability of the proposed algorithms.
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Received: 13 December 2016
Published: 26 May 2017
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| Fund:The National Natural Science Foundation of China (61562091, 61472345), The Natural Science Foundation of Yunnan Province (2014FA023, 2016FB110), The Foundation of Backbone Teacher Development of Yunnan University, The Program for Excellent Young Talents of Yunnan University (XT412003), The Open Foundation of Key Laboratory of Software Engineering of Yunnan Province (2012SE303, 2012SE205) |
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Corresponding Authors:
YUE Kun
E-mail: kyue@ynu.edu.cn
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