中文摘要：

度量用户间影响力对商品的营销和推广具有重要作用。然而,已有工作通常假设用户之间的相互影响行为是独立的,忽略了影响力在传播过程中具有的累积效应。为解决此问题,该文在线性阈值模型的框架下,提出一种影响力传播权重的计算方法。该方法将社交网络中用户的历史行为日志看作样本,借鉴最大似然估计的思想对用户间影响力学习问题建模,并设计一种优化的粒子群算法对问题求解。实验使用真实数据验证了该方法的有效性。

英文摘要：

Quantizing the influence propagation weights between users plays an important role in commodity marketing and promoting in social networks. However, most of current studies assume the mutual behaviors between users to influence each other are independent, while overlooked the accumulative effect in influence propagation process. To fill this gap, this study proposes an influence weights learning approach under the framework of the linear threshold model. With a log of past propagations of involved users in social networks, the study formulize an objective function on the basis of maximum likelihood estimation for the proposed problem, and presents a particle swarm optimization algorithm according to the objective function. Experimental results on real-world datasets validate the effectiveness of the proposed approach.