Topological Recurrent Neural Network for Diffusion Prediction

Information diffusion is a common phenomenon on social networks [1], [2]. Its modeling has many applications, such as helping to predict which user is an opinion leader [3], how much a cascade will grow [4], who are the diffusion sources [5], which user will digg a particular story [6], and so on. In this paper, we study the task of information diffusion prediction. The goal is to design an effective diffusion model, which can estimate the activation probability for an inactive node in a cascade. We consider the most standard setting of information diffusion, where we have inputs of: 1) a data graph G (V, E), where V is the set of nodes and E is the set of edges; 2) a set of cascade sequences, each of which is an ordered sequence of node activation over V. For example, in Figure 1, the data graph G is a network of seven nodes; a cascade sequence A B C D is a sequence of nodes ordered by their activation time stamps. Early work assumes diffusion model as given, such as independent cascade (IC) and linear threshold (LT) [3]. There are many extensions of the IC and LT models, such as continuoustime IC [7].