Cascade-Enhanced Graph Convolutional Network for Information Diffusion Prediction

Information diffusion prediction aims to estimate the probability of an inactive user to be activated next in an information diffusion cascade. Existing works predict future user activation either by capturing sequential dependencies within the cascade or leveraging rich graph connections among users. However, most of them perform prediction based on user correlations within the current cascade without fully exploiting diffusion properties from other cascades, which may contain beneficial collaborative patterns for the current cascade. In this paper, we propose a novel Cascade-Enhanced Graph Convolutional Networks (CE-GCN), effectively exploiting collaborative patterns over cascades to enhance the prediction of future infections in the target cascade. Specifically, we explicitly integrate cascades into diffusion process modeling via a heterogeneous graph. Then, the collaborative patterns are explicitly injected into unified user embedding by message passing. Besides, we design a cascade-specific aggregator to adaptively refine user embeddings by modeling different effects of collaborative features from other cascades with the guidance of user context and time context in the current cascade. Extensive experiments on three public datasets demonstrate the effectiveness of the proposed model.