中文摘要：

文中针对社交网络中特定用户展开个性化关键传播用户挖掘研究，目标是在线性阈值传播模型的基础上，挖掘出能够最大程度影响网络中特定用户的节点集合。尽管在社交网络影响最大化问题方面已存在相关工作，但该文工作偏重于针对网络中的特定用户展开，该问题的解决将有助于企业有效的进行个性化产品营销。为此，文中提出一种基于LT模型的个性化关键传播用户挖掘问题的解决框架。首先，在线性阈值模型的基本传播机制下，提出一个随机函数来模拟基于LT模型的个性化关键传播用户挖掘问题的目标函数，该随机函数具有较小方差的理论保证；然后，提出一个有效的求解算法从网络中挖掘针对特定用户的关键传播节点集合，理论证明该算法具有（1-1/e）的近似精度保证。实验使用真实的社交网络数据验证了算法的有效性。

英文摘要：

In this paper,we study a new problem of personalized key propagating users miningbased on LT model.This problem aims to mine out the most influential nodes under the basic linearthreshold model for a target user in the social network,which will in favor of better productmarketing.While different from existing research on influence maximization area focuses onidentifying a seed set to maximize the influence spread over the entire network,this problemfocuses on identifying a seed set which can maximize the influence spread to a given target user.For this purpose,we present a solution framework for the proposed target-based influencemaximization problem.Specially,we first provide a random function to randomly simulate theobjective function of our problem with low variance guarantee.Then,we present an efficientalgorithm to identify influential nodes for a given target user with approximation guarantee（1-1/e）.Experimental results on several real-world social networks validate the performance ofthe proposed algorithms.