GRASS: Learning Spatial–Temporal Properties From Chainlike Cascade Data for Microscopic Diffusion Prediction

Information diffusion prediction captures diffusion dynamics of online messages in social networks. Thus, it is the basis of many essential tasks such as popularity prediction and viral marketing. However, there are two thorny problems caused by the loss of spatial–temporal properties of cascade data: “position-hopping” and “branch-independency.” The former means no exact propagation relationship between any two consecutive infected users. The latter indicates that not all previously infected users contribute to the prediction of the next infected user. This article proposes the GRU-like Attention Unit and Structural Spreading (GRASS) model for microscopic cascade prediction to overcome the above two problems. First, we introduce the attention mechanism into the gated recurrent unit (GRU) component to expand the restricted receptive field of the recurrent neural network (RNN)-type module, thus addressing the “position-hopping” problem. Second, the structural spreading (SS) mechanism leverages structural features to filter out related users and controls the generation of cascade hidden states, thereby solving the “branch-independency” problem. Experiments on multiple real-world datasets show that our model significantly outperforms state-of-the-art baseline models on both ${\text{ hits@}}\kappa $ and ${\text{ map@}}\kappa $ metrics. Furthermore, the visualization of latent representations by t-distributed stochastic neighbor embedding (t-SNE) indicates that our model makes different cascades more discriminative during the encoding process.