Influence Weights Learning under Linear Threshold Model in Social Networks

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.