# HierCas: Hierarchical Temporal Graph Attention Networks for Popularity Prediction in Information Cascades

arXiv: https://arxiv.org/abs/2310.13219 · HTML: https://mlsanity.com/paper/2310.13219/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 37 references that are in the map.
- First posted on arXiv 20 Oct 2023.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Inductive Representation Learning on Temporal Graphs](https://mlsanity.com/paper/2002.07962/) (2020)
- [DeepInf: Social Influence Prediction with Deep Learning](https://mlsanity.com/paper/1807.05560/) (2018)
- [The science of fake news](https://mlsanity.com/paper/2307.07903/) (2023)
- [DeepCas: an End-to-end Predictor of Information Cascades](https://mlsanity.com/paper/1611.05373/) (2016)
- [Can Cascades be Predicted?](https://mlsanity.com/paper/1403.4608/) (2014)
- [Topological Recurrent Neural Network for Diffusion Prediction](https://mlsanity.com/paper/1711.10162/) (2017)
- [Feature Driven and Point Process Approaches for Popularity Prediction](https://mlsanity.com/paper/1608.04862/) (2016)
- [CasSeqGCN: Combining Network Structure and Temporal Sequence to Predict Information Cascades](https://mlsanity.com/paper/2110.06836/) (2021)
- [Predictability of Popularity: Gaps between Prediction and Understanding](https://mlsanity.com/paper/1603.09436/) (2016)
- [Continuous-Time Graph Learning for Cascade Popularity Prediction](https://mlsanity.com/paper/2306.03756/) (2023)
- [CasGCN: Predicting future cascade growth based on information diffusion graph](https://mlsanity.com/paper/2009.05152/) (2020)

## Abstract (arXiv)

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.