Casformer: Information Popularity Prediction With Adaptive Cascade Sampling and Graph Transformer in Social Networks

Abstract

Predicting the popularity of information in social networks is crucial for effective social marketing and recommendation systems. However, accurately comprehending the complex dynamics of information diffusion remains a challenging task. Existing methods, including feature-based approaches, point process models, and deep learning techniques, often fail to capture the fine-grained features of information cascades, such as dynamic diffusion patterns, cascade statistics, and the interplay between spatial and temporal information. To address these limitations, we propose Casformer, a novel graph-based Transformer architecture that effectively learns both micro-level time-aware structural information and macro-level long-term influence along the information propagation process. Casformer employs a cascade attention network (CAT) to capture the micro-level features and a Transformer model to learn the macro-level influence. Furthermore, we introduce an adaptive cascade graph sampling strategy based on the temporal diffusion pattern and cascade statistics of information to obtain the most informative cascade graph sequence. By leveraging multi-level fine-grained evolving features of information cascades, Casformer achieves high accuracy in information popularity prediction. Experimental results on real-world social network and scientific citation network datasets demonstrate the effectiveness and superiority of Casformer compared to state-of-the-art methods in information popularity prediction.