Hierarchical attention neural network for information cascade prediction

Online social networking platforms have drastically facilitated the phenomenon of information cascades, making cascade prediction an important task for both researchers and practitioners. In cascade propagation paths, influential users can often attract more attention. Moreover, the community structure formed by users with similar interests creates information redundancy, thereby restricting the propagation process. These two features greatly affect the growth of the cascades. This paper investigates the incremental prediction of cascades during a future time period based on the evolution of cascades in early stages and proposes a neural network framework with hierarchical attention mechanisms, named Hierarchical Attention Cascade Neural Network (CasHAN). This network has a node-level attention mechanism based on user influence and a sequence-level attention mechanism based on community redundancy. User influence considers the user's own attributes and the influence feedback provided by neighbors, while community redundancy measures information redundancy characteristics that limit cascade propagation. Through hierarchical attention mechanisms, the effective combination of these two features improves the accuracy of cascade increment prediction. Extensive experiments on real-world datasets demonstrate that our approach outperforms other state-of-the-art prediction methods.