A Novel Embedding Method for Information Diffusion Prediction in Social Network Big Data

With the increase of social networking websites and the interaction frequency among users, the prediction of information diffusion is required to support effective generalization and efficient inference in the context of social big data era. However, the existing models either rely on expensive probabilistic modeling of information diffusion based on partially known network structures, or discover the implicit structures of diffusion from users' behaviors without considering the impacts of different diffused contents. To address the issues, in this paper, we propose a novel information-dependent embedding-based diffusion prediction (IEDP) model to map the users in observed diffusion process into a latent embedding space, then the temporal order of users with the timestamps in the cascade can be preserved by the embedding distance of users. Our proposed model further learns the propagation probability of information in the cascade as a function of the relative positions of information-specific user embeddings in the information-dependent subspace. Then, the problem of temporal propagation prediction can be converted into the task of spatial probability learning in the embedding space. Moreover, we present an efficient margin-based optimization algorithm with a fast computation to make the inference of the information diffusion in the latent embedding space. When applying our proposed method to several social network datasets, the experimental results show the effectiveness of our proposed approach for the information diffusion prediction and the efficiency with respect to the inference speed compared with the state-of-the-art methods.