Uncertain Influence Sources Oriented Influence Blocking Maximization in Social Networks

LI Jin; YUE Kun; YOU Jie; XIE Xiaorui; ZHANG Yunfei

doi:10.11999/JEIT161360

Volume 39 Issue 9

Sep. 2017

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LI Jin, YUE Kun, YOU Jie, XIE Xiaorui, ZHANG Yunfei. Uncertain Influence Sources Oriented Influence Blocking Maximization in Social Networks[J]. Journal of Electronics & Information Technology, 2017, 39(9): 2063-2070. doi: 10.11999/JEIT161360

Citation:

LI Jin, YUE Kun, YOU Jie, XIE Xiaorui, ZHANG Yunfei. Uncertain Influence Sources Oriented Influence Blocking Maximization in Social Networks[J]. Journal of Electronics & Information Technology, 2017, 39(9): 2063-2070. doi: 10.11999/JEIT161360

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Uncertain Influence Sources Oriented Influence Blocking Maximization in Social Networks

doi: 10.11999/JEIT161360

Funds:

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)

Received Date: 2016-12-13
Rev Recd Date: 2017-04-11
Publish Date: 2017-09-19

Abstract

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.
- Social networks,
- Uncertain influence sources,
- Influence blocking maximization,
- Competitive linear threshold model,
- Sampling average approximation

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References(22)

References

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