HierCas: Hierarchical Temporal Graph Attention Networks for Popularity Prediction in Information Cascades
This work addresses the challenge of generalizable popularity prediction for applications like fake news detection and recommendations, though it is incremental by building on neural network approaches.
The paper tackles the problem of predicting popularity in information cascades by proposing HierCas, a hierarchical temporal graph attention network that operates on entire cascade graphs to capture dynamic information, achieving significant performance improvements over state-of-the-art methods on two real-world datasets.
Information cascade popularity prediction is critical for many applications, including but not limited to identifying fake news and accurate recommendations. Traditional feature-based methods heavily rely on handcrafted features, which are domain-specific and lack generalizability to new domains. To address this problem, researchers have turned to neural network-based approaches. However, most existing methods follow a sampling-based modeling approach, potentially losing continuous dynamic information that emerges during the information diffusion process. In this paper, we propose Hierarchical Temporal Graph Attention Networks for cascade popularity prediction (HierCas), which operates on the entire cascade graph by a dynamic graph modeling approach. By leveraging time-aware node embedding, graph attention mechanisms, and hierarchical pooling structures, HierCas effectively captures the popularity trend implicit in the complex cascade. Extensive experiments conducted on two real-world datasets in different scenarios demonstrate that our HierCas significantly outperforms the state-of-the-art approaches. We have released our code at https://github.com/Daisy-zzz/HierCas.