  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.
