面向不确定性影响源的社会网络影响力传播抑制方法

社会网络中影响力传播的有效抑制是社会网络影响力传播机制研究所关注的问题之一。该文针对未知影响传播源，或传播源信息具有不确定性的情况，提出面向不确定性影响源的影响力传播抑制问题。首先，为有效提高抑制算法的执行效率，讨论竞争线性阈值传播模型下影响源传播能力的近似估计方法，进而提出有限影响源情况下，期望抑制效果最大化的抑制种子集挖掘算法。其次，对于大尺寸不确定性影响源的情况，考虑算法运行效率和抑制效果之间的有效折中，提出基于抽样平均近似的期望抑制效果最大化的抑制种子集挖掘算法。最后，在真实的社会网络数据集上，通过实验测试验证了所提出方法的有效性。

Uncertain Influence Sources Oriented Influence Blocking Maximization in Social Networks

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.