Latent influence propagation on dynamic networks

Abstract

With the proliferation of diversified social network services, understanding how the influence is propagated helps us better understand the network evolution mechanism and the social impact of different kinds of information. Existing models are mostly built on the static network structure. They fail to catch the temporal dynamic property of social network. In this paper, we design a new kind of latent influence propagation, and propose a general framework based on latent feature model which performs the influence propagation on dynamic social network. Extensive experiments demonstrate our model can achieve better approximation of influence propagation and more accurate link prediction.