Information cascades prediction with attention neural network

Cascade prediction helps us uncover the basic mechanisms that govern collective human behavior in networks, and it also is very important in extensive other applications, such as viral marketing, online advertising, and recommender systems. However, it is not trivial to make predictions due to the myriad factors that influence a user's decision to reshare content. This paper presents a novel method for predicting the increment size of the information cascade based on an end-to-end neural network. Learning the representation of a cascade in an end-to-end manner circumvents the difficulties inherent to blue the design of hand-crafted features. An attention mechanism, which consists of the intra-attention and inter-gate module, was designed to obtain and fuse the temporal and structural information learned from the observed period of the cascade. The experiments were performed on two real-world scenarios, i.e., predicting the size of retweet cascades on Twitter and predicting the citation of papers in AMiner. Extensive results demonstrated that our method outperformed the state-of-the-art cascade prediction methods, including both feature-based and generative approaches.